library(swimplot) library(grid) library(gtable) library(readr) library(mosaic) library(dplyr) library(survival) library(survminer) library(ggplot2) library(scales) library(coxphf) library(ggthemes) library(tidyverse) library(gtsummary) library(flextable) library(reshape2) library(parameters) library(car) library(ComplexHeatmap) library(tidyverse) library(readxl) library(janitor) library(DT) library(pROC) library(rms)

#ctDNA Detection Rates by Window and Stages

#ctDNA at Baseline
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_data$ctDNA.Base <- factor(circ_data$ctDNA.Base, levels=c("NEGATIVE","POSITIVE"))
circ_data <- subset(circ_data, ctDNA.Base %in% c("NEGATIVE", "POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I/II","III/IVA/IVB","IVC"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.Base == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.Base, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.Base == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#ctDNA at MRD
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I/II","III/IVA/IVB","IVC"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.MRD == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.MRD, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.MRD == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#ctDNA at Surveillance
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I/II","III/IVA/IVB","IVC"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.Surveillance == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.Surveillance, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.Surveillance == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#Demographics Table

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]

circ_data_subset <- circ_data %>%
  select(
    Sex,
    Age,
    Tobacco.History,
    Prim.Location,
    cT,
    cN,
    cM,
    Histology,
    Stage,
    p16.status,
    Treatment.Group,
    PFS.Event,
    OS.Event,
    OS.months) %>%
  mutate(
    Sex = factor(Sex),
    Age = as.numeric(Age),
    Tobacco.History = factor(Tobacco.History),
    Prim.Location = factor(Prim.Location),
    cT = factor(cT),
    cN = factor(cN),
    cM = factor(cM),
    Histology = factor(Histology),
    Stage = factor(Stage),
    p16.status = factor(p16.status),
    Treatment.Group = factor(Treatment.Group),
    PFS.Event = factor(PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression")),
    OS.Event = factor(OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased")),
    OS.months = as.numeric(OS.months)) 
table1 <- circ_data_subset %>%
  tbl_summary(
    statistic = list(
      all_continuous() ~ "{median} ({min} - {max})",
      all_categorical() ~ "{n} ({p}%)")) %>%
  bold_labels()
table1
Characteristic N = 971
Sex
    Female 17 (18%)
    Male 80 (82%)
Age 66 (29 - 95)
Tobacco.History 63 (65%)
Prim.Location
    Larynx/Hypopharynx 5 (5.2%)
    Oral cavity 16 (16%)
    Oropharynx 67 (69%)
    Other (paranasal sinus and nasopharyngeal) 9 (9.3%)
cT
    T0 2 (2.1%)
    T1 12 (12%)
    T2 31 (32%)
    T3 30 (31%)
    T4 21 (22%)
    TX 1 (1.0%)
cN
    N0 22 (23%)
    N1 33 (34%)
    N2 33 (34%)
    N3 9 (9.3%)
cM
    M0 93 (96%)
    M1 4 (4.1%)
Histology
    Adenosquamous carcinoma 1 (1.0%)
    Basaloid squamous cell carcinoma 6 (6.2%)
    Epithelial myoepithelial carcinoma 1 (1.0%)
    Squamous cell carcinoma 86 (89%)
    Undifferentiated carcinoma 3 (3.1%)
Stage
    I/II 49 (51%)
    III/IVA/IVB 45 (46%)
    IVC 3 (3.1%)
p16.status
    Negative 43 (44%)
    Positive 54 (56%)
Treatment.Group
    Definitive CRT or RT 69 (71%)
    None (Declined Treatment) 1 (1.0%)
    None (Hospice) 2 (2.1%)
    Surgery + CRT or RT 24 (25%)
    Surgery only 1 (1.0%)
PFS.Event
    No Progression 64 (66%)
    Progression 33 (34%)
OS.Event
    Alive 81 (84%)
    Deceased 16 (16%)
OS.months 22 (2 - 56)
1 n (%); Median (Min - Max) 
fit1 <- as_flex_table(
  table1,
  include = everything(),
  return_calls = FALSE
)
fit1

Characteristic

N = 971

Sex

Female

17 (18%)

Male

80 (82%)

Age

66 (29 - 95)

Tobacco.History

63 (65%)

Prim.Location

Larynx/Hypopharynx

5 (5.2%)

Oral cavity

16 (16%)

Oropharynx

67 (69%)

Other (paranasal sinus and nasopharyngeal)

9 (9.3%)

cT

T0

2 (2.1%)

T1

12 (12%)

T2

31 (32%)

T3

30 (31%)

T4

21 (22%)

TX

1 (1.0%)

cN

N0

22 (23%)

N1

33 (34%)

N2

33 (34%)

N3

9 (9.3%)

cM

M0

93 (96%)

M1

4 (4.1%)

Histology

Adenosquamous carcinoma

1 (1.0%)

Basaloid squamous cell carcinoma

6 (6.2%)

Epithelial myoepithelial carcinoma

1 (1.0%)

Squamous cell carcinoma

86 (89%)

Undifferentiated carcinoma

3 (3.1%)

Stage

I/II

49 (51%)

III/IVA/IVB

45 (46%)

IVC

3 (3.1%)

p16.status

Negative

43 (44%)

Positive

54 (56%)

Treatment.Group

Definitive CRT or RT

69 (71%)

None (Declined Treatment)

1 (1.0%)

None (Hospice)

2 (2.1%)

Surgery + CRT or RT

24 (25%)

Surgery only

1 (1.0%)

PFS.Event

No Progression

64 (66%)

Progression

33 (34%)

OS.Event

Alive

81 (84%)

Deceased

16 (16%)

OS.months

22 (2 - 56)

1n (%); Median (Min - Max)

save_as_docx(fit1, path= "~/Downloads/1. CLIA HNSCC UNM Demographics Table.docx")

#Demographics Table by ctDNA at baseline

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]

circ_data_subset1 <- circ_data %>%
  select(
    Sex,
    Age,
    Tobacco.History,
    Prim.Location,
    cT,
    cN,
    cM,
    Histology,
    Stage,
    p16.status,
    Treatment.Group,
    PFS.Event,
    OS.Event,
    OS.months) %>%
  mutate(
    Sex = factor(Sex),
    Age = as.numeric(Age),
    Tobacco.History = factor(Tobacco.History),
    Prim.Location = factor(Prim.Location),
    cT = factor(cT),
    cN = factor(cN),
    cM = factor(cM),
    Histology = factor(Histology),
    Stage = factor(Stage),
    p16.status = factor(p16.status),
    Treatment.Group = factor(Treatment.Group),
    PFS.Event = factor(PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression")),
    OS.Event = factor(OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased")),
    OS.months = as.numeric(OS.months)) 

circ_data1 <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]

circ_data_subset2 <- circ_data1 %>%
  select(
    Sex,
    Age,
    Tobacco.History,
    Prim.Location,
    cT,
    cN,
    cM,
    Histology,
    Stage,
    p16.status,
    Treatment.Group,
    PFS.Event,
    OS.Event,
    OS.months,
    ctDNA.Base) %>%
  mutate(
    Sex = factor(Sex),
    Age = as.numeric(Age),
    Tobacco.History = factor(Tobacco.History),
    Prim.Location = factor(Prim.Location),
    cT = factor(cT),
    cN = factor(cN),
    cM = factor(cM),
    Histology = factor(Histology),
    Stage = factor(Stage),
    p16.status = factor(p16.status),
    Treatment.Group = factor(Treatment.Group),
    PFS.Event = factor(PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression")),
    OS.Event = factor(OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased")),
    OS.months = as.numeric(OS.months),
    ctDNA.Base = factor(ctDNA.Base, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive")))
Overall <- circ_data_subset1 %>%
  tbl_summary(
    statistic = list(
      all_continuous() ~ "{median} ({min} - {max})",
      all_categorical() ~ "{n} ({p}%)")) %>%
  bold_labels()
Overall
Characteristic N = 971
Sex
    Female 17 (18%)
    Male 80 (82%)
Age 66 (29 - 95)
Tobacco.History 63 (65%)
Prim.Location
    Larynx/Hypopharynx 5 (5.2%)
    Oral cavity 16 (16%)
    Oropharynx 67 (69%)
    Other (paranasal sinus and nasopharyngeal) 9 (9.3%)
cT
    T0 2 (2.1%)
    T1 12 (12%)
    T2 31 (32%)
    T3 30 (31%)
    T4 21 (22%)
    TX 1 (1.0%)
cN
    N0 22 (23%)
    N1 33 (34%)
    N2 33 (34%)
    N3 9 (9.3%)
cM
    M0 93 (96%)
    M1 4 (4.1%)
Histology
    Adenosquamous carcinoma 1 (1.0%)
    Basaloid squamous cell carcinoma 6 (6.2%)
    Epithelial myoepithelial carcinoma 1 (1.0%)
    Squamous cell carcinoma 86 (89%)
    Undifferentiated carcinoma 3 (3.1%)
Stage
    I/II 49 (51%)
    III/IVA/IVB 45 (46%)
    IVC 3 (3.1%)
p16.status
    Negative 43 (44%)
    Positive 54 (56%)
Treatment.Group
    Definitive CRT or RT 69 (71%)
    None (Declined Treatment) 1 (1.0%)
    None (Hospice) 2 (2.1%)
    Surgery + CRT or RT 24 (25%)
    Surgery only 1 (1.0%)
PFS.Event
    No Progression 64 (66%)
    Progression 33 (34%)
OS.Event
    Alive 81 (84%)
    Deceased 16 (16%)
OS.months 22 (2 - 56)
1 n (%); Median (Min - Max) 

ByctDNA_MRD <- circ_data_subset2 %>%
  tbl_summary(
    by = ctDNA.Base, # add this line to subgroup by ctDNA.Base
    statistic = list(
      all_continuous() ~ "{median} ({min} - {max})",
      all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels()
36 missing rows in the "ctDNA.Base" column have been removed.
ByctDNA_MRD
Characteristic Negative
N = 71		 Positive
N = 551		 p-value2
Sex

0.10
    Female 3 (43%) 8 (15%)
    Male 4 (57%) 47 (85%)
Age 80 (53 - 95) 65 (37 - 95) 0.081
Tobacco.History 4 (57%) 37 (67%) 0.7
Prim.Location

0.037
    Larynx/Hypopharynx 0 (0%) 3 (5.5%)
    Oral cavity 3 (43%) 4 (7.3%)
    Oropharynx 3 (43%) 44 (80%)
    Other (paranasal sinus and nasopharyngeal) 1 (14%) 4 (7.3%)
cT

0.050
    T0 0 (0%) 2 (3.6%)
    T1 1 (14%) 4 (7.3%)
    T2 2 (29%) 19 (35%)
    T3 0 (0%) 21 (38%)
    T4 4 (57%) 9 (16%)
    TX 0 (0%) 0 (0%)
cN

>0.9
    N0 1 (14%) 11 (20%)
    N1 3 (43%) 19 (35%)
    N2 3 (43%) 19 (35%)
    N3 0 (0%) 6 (11%)
cM

>0.9
    M0 7 (100%) 53 (96%)
    M1 0 (0%) 2 (3.6%)
Histology

0.14
    Adenosquamous carcinoma 0 (0%) 0 (0%)
    Basaloid squamous cell carcinoma 0 (0%) 3 (5.5%)
    Epithelial myoepithelial carcinoma 0 (0%) 0 (0%)
    Squamous cell carcinoma 6 (86%) 52 (95%)
    Undifferentiated carcinoma 1 (14%) 0 (0%)
Stage

0.4
    I/II 2 (29%) 32 (58%)
    III/IVA/IVB 5 (71%) 21 (38%)
    IVC 0 (0%) 2 (3.6%)
p16.status

0.090
    Negative 5 (71%) 18 (33%)
    Positive 2 (29%) 37 (67%)
Treatment.Group

0.2
    Definitive CRT or RT 5 (71%) 51 (93%)
    None (Declined Treatment) 0 (0%) 0 (0%)
    None (Hospice) 0 (0%) 0 (0%)
    Surgery + CRT or RT 2 (29%) 3 (5.5%)
    Surgery only 0 (0%) 1 (1.8%)
PFS.Event

0.4
    No Progression 6 (86%) 35 (64%)
    Progression 1 (14%) 20 (36%)
OS.Event

0.3
    Alive 7 (100%) 44 (80%)
    Deceased 0 (0%) 11 (20%)
OS.months 31 (21 - 39) 16 (2 - 45) 0.028
1 n (%); Median (Min - Max)
2 Fisher’s exact test; Wilcoxon rank sum test 

merged_table <- tbl_merge(tbls=list(Overall, ByctDNA_MRD))
merged_table
Characteristic
Table 1
Table 2
N = 971 Negative
N = 71		 Positive
N = 551		 p-value2
Sex


0.10
    Female 17 (18%) 3 (43%) 8 (15%)
    Male 80 (82%) 4 (57%) 47 (85%)
Age 66 (29 - 95) 80 (53 - 95) 65 (37 - 95) 0.081
Tobacco.History 63 (65%) 4 (57%) 37 (67%) 0.7
Prim.Location


0.037
    Larynx/Hypopharynx 5 (5.2%) 0 (0%) 3 (5.5%)
    Oral cavity 16 (16%) 3 (43%) 4 (7.3%)
    Oropharynx 67 (69%) 3 (43%) 44 (80%)
    Other (paranasal sinus and nasopharyngeal) 9 (9.3%) 1 (14%) 4 (7.3%)
cT


0.050
    T0 2 (2.1%) 0 (0%) 2 (3.6%)
    T1 12 (12%) 1 (14%) 4 (7.3%)
    T2 31 (32%) 2 (29%) 19 (35%)
    T3 30 (31%) 0 (0%) 21 (38%)
    T4 21 (22%) 4 (57%) 9 (16%)
    TX 1 (1.0%) 0 (0%) 0 (0%)
cN


>0.9
    N0 22 (23%) 1 (14%) 11 (20%)
    N1 33 (34%) 3 (43%) 19 (35%)
    N2 33 (34%) 3 (43%) 19 (35%)
    N3 9 (9.3%) 0 (0%) 6 (11%)
cM


>0.9
    M0 93 (96%) 7 (100%) 53 (96%)
    M1 4 (4.1%) 0 (0%) 2 (3.6%)
Histology


0.14
    Adenosquamous carcinoma 1 (1.0%) 0 (0%) 0 (0%)
    Basaloid squamous cell carcinoma 6 (6.2%) 0 (0%) 3 (5.5%)
    Epithelial myoepithelial carcinoma 1 (1.0%) 0 (0%) 0 (0%)
    Squamous cell carcinoma 86 (89%) 6 (86%) 52 (95%)
    Undifferentiated carcinoma 3 (3.1%) 1 (14%) 0 (0%)
Stage


0.4
    I/II 49 (51%) 2 (29%) 32 (58%)
    III/IVA/IVB 45 (46%) 5 (71%) 21 (38%)
    IVC 3 (3.1%) 0 (0%) 2 (3.6%)
p16.status


0.090
    Negative 43 (44%) 5 (71%) 18 (33%)
    Positive 54 (56%) 2 (29%) 37 (67%)
Treatment.Group


0.2
    Definitive CRT or RT 69 (71%) 5 (71%) 51 (93%)
    None (Declined Treatment) 1 (1.0%) 0 (0%) 0 (0%)
    None (Hospice) 2 (2.1%) 0 (0%) 0 (0%)
    Surgery + CRT or RT 24 (25%) 2 (29%) 3 (5.5%)
    Surgery only 1 (1.0%) 0 (0%) 1 (1.8%)
PFS.Event


0.4
    No Progression 64 (66%) 6 (86%) 35 (64%)
    Progression 33 (34%) 1 (14%) 20 (36%)
OS.Event


0.3
    Alive 81 (84%) 7 (100%) 44 (80%)
    Deceased 16 (16%) 0 (0%) 11 (20%)
OS.months 22 (2 - 56) 31 (21 - 39) 16 (2 - 45) 0.028
1 n (%); Median (Min - Max)
2 Fisher’s exact test; Wilcoxon rank sum test 

fit1 <- as_flex_table(
  merged_table,
  include = everything(),
  return_calls = FALSE
)
fit1

Table 1

Table 2

Characteristic

N = 971

Negative
N = 71

Positive
N = 551

p-value2

Sex

0.10

Female

17 (18%)

3 (43%)

8 (15%)

Male

80 (82%)

4 (57%)

47 (85%)

Age

66 (29 - 95)

80 (53 - 95)

65 (37 - 95)

0.081

Tobacco.History

63 (65%)

4 (57%)

37 (67%)

0.7

Prim.Location

0.037

Larynx/Hypopharynx

5 (5.2%)

0 (0%)

3 (5.5%)

Oral cavity

16 (16%)

3 (43%)

4 (7.3%)

Oropharynx

67 (69%)

3 (43%)

44 (80%)

Other (paranasal sinus and nasopharyngeal)

9 (9.3%)

1 (14%)

4 (7.3%)

cT

0.050

T0

2 (2.1%)

0 (0%)

2 (3.6%)

T1

12 (12%)

1 (14%)

4 (7.3%)

T2

31 (32%)

2 (29%)

19 (35%)

T3

30 (31%)

0 (0%)

21 (38%)

T4

21 (22%)

4 (57%)

9 (16%)

TX

1 (1.0%)

0 (0%)

0 (0%)

cN

>0.9

N0

22 (23%)

1 (14%)

11 (20%)

N1

33 (34%)

3 (43%)

19 (35%)

N2

33 (34%)

3 (43%)

19 (35%)

N3

9 (9.3%)

0 (0%)

6 (11%)

cM

>0.9

M0

93 (96%)

7 (100%)

53 (96%)

M1

4 (4.1%)

0 (0%)

2 (3.6%)

Histology

0.14

Adenosquamous carcinoma

1 (1.0%)

0 (0%)

0 (0%)

Basaloid squamous cell carcinoma

6 (6.2%)

0 (0%)

3 (5.5%)

Epithelial myoepithelial carcinoma

1 (1.0%)

0 (0%)

0 (0%)

Squamous cell carcinoma

86 (89%)

6 (86%)

52 (95%)

Undifferentiated carcinoma

3 (3.1%)

1 (14%)

0 (0%)

Stage

0.4

I/II

49 (51%)

2 (29%)

32 (58%)

III/IVA/IVB

45 (46%)

5 (71%)

21 (38%)

IVC

3 (3.1%)

0 (0%)

2 (3.6%)

p16.status

0.090

Negative

43 (44%)

5 (71%)

18 (33%)

Positive

54 (56%)

2 (29%)

37 (67%)

Treatment.Group

0.2

Definitive CRT or RT

69 (71%)

5 (71%)

51 (93%)

None (Declined Treatment)

1 (1.0%)

0 (0%)

0 (0%)

None (Hospice)

2 (2.1%)

0 (0%)

0 (0%)

Surgery + CRT or RT

24 (25%)

2 (29%)

3 (5.5%)

Surgery only

1 (1.0%)

0 (0%)

1 (1.8%)

PFS.Event

0.4

No Progression

64 (66%)

6 (86%)

35 (64%)

Progression

33 (34%)

1 (14%)

20 (36%)

OS.Event

0.3

Alive

81 (84%)

7 (100%)

44 (80%)

Deceased

16 (16%)

0 (0%)

11 (20%)

OS.months

22 (2 - 56)

31 (21 - 39)

16 (2 - 45)

0.028

1n (%); Median (Min - Max)

2Fisher's exact test; Wilcoxon rank sum test

save_as_docx(fit1, path = "~/Downloads/1b. CLIA HNSCC UNM Demographics Table by ctDNA.docx")

#Overview plot by Stage

setwd("~/Downloads") 
clinstage <- read.csv("CLIA HNSCC UNM_OP.csv")
clinstage_df <- as.data.frame(clinstage)

# Creating the basic swimmer plot
oplot <- swimmer_plot(df=clinstage_df,
                      id='PatientName',
                      end='fu.diff.months',
                      fill='gray',
                      width=.01,
                      base_size = 14,
                      stratify= c('Stage'))

# Adding themes and scales
oplot <- oplot + theme(panel.border = element_blank())
oplot <- oplot + scale_y_continuous(breaks = seq(0, 72, by = 3))
oplot <- oplot + labs(x ="Patients", y="Months from Diagnosis")

# Adding swimmer points
oplot_ev1 <- oplot + swimmer_points(df_points=clinstage_df,
                                    id='PatientName',
                                    time='date.diff.months',
                                    name_shape ='Event_type',
                                    name_col = 'Event',
                                    size=3.5,fill='black')
# Optionally uncomment and use col='darkgreen' if needed

# Adding shape manual scale
oplot_ev1.1 <- oplot_ev1 + ggplot2::scale_shape_manual(name="Event_type",
                                                       values=c(1,16,6,18,18,4),
                                                       breaks=c('ctDNA_neg','ctDNA_pos', 'Imaging','Surgery','Biopsy', 'Death'))

# Display the plot
oplot_ev1.1

oplot_ev2 <- oplot_ev1.1 + swimmer_lines(df_lines=clinstage_df,
                                         id='PatientName',
                                         start='Tx_start.months',
                                         end='Tx_end.months',
                                         name_col='Tx_type',
                                         size=3.5,
                                         name_alpha = 1.0)
oplot_ev2 <- oplot_ev2 + guides(linetype = guide_legend(override.aes = list(size = 5, color = "black")))
oplot_ev2

oplot_ev2.2 <- oplot_ev2 + ggplot2::scale_color_manual(name="Event",values=c( "grey", "orange", "black", "black", "green", "red", "purple", "blue"))
oplot_ev2.2

#PFS in Complete Cohort (N=97)

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.available, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) ~ 
    ctDNA.available, data = circ_data)

      n events median 0.95LCL 0.95UCL
[1,] 97     33     NA      NA      NA
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.available, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue"), title="PFS - Complete Cohort (n=97)", ylab= "Progression-Free Survival", xlab="Months from Start of definitive Treatment", legend.labs=c("Complete cohort"), legend.title="")

summary(KM_curve, times= c(12, 24, 36))
Call: survfit(formula = surv_object ~ ctDNA.available, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     64      23    0.760  0.0438        0.660        0.833
   24     36       8    0.651  0.0520        0.538        0.742
   36     12       2    0.612  0.0555        0.494        0.711

#OS in Complete Cohort (N=97)

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.available, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$OS.months, event = circ_data$OS.Event) ~ 
    ctDNA.available, data = circ_data)

      n events median 0.95LCL 0.95UCL
[1,] 97     16     NA      NA      NA
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.available, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue"), title="OS - Complete Cohort (n=97)", ylab= "Overall Survival", xlab="Months from Start of definitive Treatment", legend.labs=c("Complete cohort"), legend.title="")

summary(KM_curve, times= c(12, 24, 36))
Call: survfit(formula = surv_object ~ ctDNA.available, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     73      13    0.866  0.0347        0.780        0.920
   24     42       3    0.822  0.0412        0.724        0.888
   36     17       0    0.822  0.0412        0.724        0.888

#Association of Baseline ctDNA MTM levels with clinicopathological factors

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

tally(~cStage, data=circ_data, margins = TRUE)
cStage
  I/II III/IV  Total 
    34     28     62 
circ_data$cStage <- factor(circ_data$cStage, levels = c("I/II","III/IV"), labels = c("I/II (n=34)","III/IV (n=29)"))
boxplot(ctDNA.Base.MTM~cStage, data=circ_data, main="ctDNA pre-treatment MTM - Stage", xlab="Stage", ylab="MTM/mL", col="white",border="black", ylim = c(0, 200))

median_ctDNA.Stage <- circ_data %>%
  group_by(cStage) %>%
  summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.Stage)
m1<-wilcox.test(ctDNA.Base.MTM ~ cStage, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m1)

    Wilcoxon rank sum test with continuity correction

data:  ctDNA.Base.MTM by cStage
W = 590, p-value = 0.1081
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -0.7499638 20.1599876
sample estimates:
difference in location 
              3.690372 
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

tally(~cT.status, data=circ_data, margins = TRUE)
cT.status
T0-T2 T3-T4 Total 
   28    34    62 
circ_data$cT.status <- factor(circ_data$cT.status, levels = c("T0-T2","T3-T4"), labels = c("T0-T2 (n=28)","T3-T4 (n=34)"))
boxplot(ctDNA.Base.MTM~cT.status, data=circ_data, main="ctDNA pre-treatment MTM - T stage", xlab="T stage", ylab="MTM/mL", col="white",border="black", ylim = c(0, 200))

median_ctDNA.cT <- circ_data %>%
  group_by(cT.status) %>%
  summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.cT)
m2<-wilcox.test(ctDNA.Base.MTM ~ cT.status, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m2)

    Wilcoxon rank sum test with continuity correction

data:  ctDNA.Base.MTM by cT.status
W = 466, p-value = 0.893
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -7.789979  7.539938
sample estimates:
difference in location 
            -0.1111266 
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

tally(~cT, data=circ_data, margins = TRUE)
cT
   T0    T1    T2    T3    T4 Total 
    2     5    21    21    13    62 
circ_data$cT <- factor(circ_data$cT, levels = c("T0","T1","T2","T3","T4"))
boxplot(ctDNA.Base.MTM~cT, data=circ_data, main="ctDNA pre-treatment MTM - cT status", xlab="cT status", ylab="MTM/mL", col="white",border="black", ylim = c(0, 200))

median_ctDNA.cT <- circ_data %>%
  group_by(cT) %>%
  summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.cT)
pairwise_wilcox <- pairwise.wilcox.test(circ_data$ctDNA.Base.MTM, circ_data$cT, 
                                        p.adjust.method = "none", 
                                        exact = FALSE)
print(pairwise_wilcox)

    Pairwise comparisons using Wilcoxon rank sum test with continuity correction 

data:  circ_data$ctDNA.Base.MTM and circ_data$cT 

   T0   T1   T2   T3  
T1 0.85 -    -    -   
T2 0.21 0.85 -    -   
T3 0.14 0.65 0.69 -   
T4 0.55 0.62 0.36 0.23

P value adjustment method: none 
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available == "TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base != "",]
circ_data$cT <- factor(circ_data$cT, levels = c("T0", "T1", "T2", "T3", "T4"))
circ_data$ctDNA.Base.MTM <- as.numeric(circ_data$ctDNA.Base.MTM)
cT_levels <- levels(circ_data$cT)
p_value_matrix <- matrix(NA, nrow = length(cT_levels), ncol = length(cT_levels))
rownames(p_value_matrix) <- cT_levels
colnames(p_value_matrix) <- cT_levels

for (i in 1:length(cT_levels)) {
  for (j in i:length(cT_levels)) {
    if (i != j) {
      # Extract data for both groups
      data1 <- circ_data %>% filter(cT == cT_levels[i]) %>% pull(ctDNA.Base.MTM)
      data2 <- circ_data %>% filter(cT == cT_levels[j]) %>% pull(ctDNA.Base.MTM)
      
      # Perform Wilcoxon test and store p-value
      test_result <- wilcox.test(data1, data2, exact = FALSE)
      p_value_matrix[i, j] <- test_result$p.value
      p_value_matrix[j, i] <- test_result$p.value  # Make symmetric
    } else {
      p_value_matrix[i, j] <- 1  # Self-comparison = 1
    }
  }
}

p_value_matrix[is.na(p_value_matrix)] <- 1.00
p_value_data <- melt(p_value_matrix)
colnames(p_value_data) <- c("cT1", "cT2", "p_value")
p_value_data <- p_value_data %>%
  mutate(
    significance = case_when(
      p_value < 0.001 ~ "***",
      p_value < 0.01 ~ "**",
      p_value < 0.05 ~ "*",
      TRUE ~ ""
    )
  )

ggplot(p_value_data, aes(x = cT1, y = cT2, fill = p_value)) +
  geom_tile(color = "white", size = 0.8) +  # Thicker grid lines for separation
  geom_text(aes(label = significance), color = "black", size = 6, fontface = "bold") +  # Significance markers
  scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0.05) +  # Gradient colors
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 12, face = "bold"),
        axis.text.y = element_text(size = 12, face = "bold"),
        panel.grid = element_blank()) +
  labs(title = "Pairwise Wilcoxon-Test P-Values (ctDNA.Base.MTM by cT)",
       x = "cT Status", y = "cT Status", fill = "P-Value")
G2;H2;Warningh: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_]8;;ide:run:warnings()warnings()]8;;` to see where this warning was generated.g

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

tally(~cN.status, data=circ_data, margins = TRUE)
cN.status
   N0 N1-N3 Total 
   12    50    62 
circ_data$cN.status <- factor(circ_data$cN.status, levels = c("N0","N1-N3"), labels = c("N0 (n=12)","N1-N3 (n=50)"))
boxplot(ctDNA.Base.MTM~cN.status, data=circ_data, main="ctDNA pre-treatment MTM - cN status", xlab="cN status", ylab="MTM/mL", col="white",border="black", ylim = c(0, 200))

median_ctDNA.cN <- circ_data %>%
  group_by(cN.status) %>%
  summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.cN)
m3<-wilcox.test(ctDNA.Base.MTM ~ cN.status, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m3)

    Wilcoxon rank sum test with continuity correction

data:  ctDNA.Base.MTM by cN.status
W = 162, p-value = 0.01422
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -42.439959  -1.050045
sample estimates:
difference in location 
             -11.19909 
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

tally(~cN, data=circ_data, margins = TRUE)
cN
   N0    N1    N2    N3 Total 
   12    22    22     6    62 
circ_data$cN <- factor(circ_data$cN, levels = c("N0","N1","N2","N3"))
boxplot(ctDNA.Base.MTM~cN, data=circ_data, main="ctDNA pre-treatment MTM - N Stage", xlab="N Stage", ylab="MTM/mL", col="white",border="black", ylim = c(0, 500))

median_ctDNA.cN <- circ_data %>%
  group_by(cN) %>%
  summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.cN)
pairwise_wilcox <- pairwise.wilcox.test(circ_data$ctDNA.Base.MTM, circ_data$cN, 
                                        p.adjust.method = "none", 
                                        exact = FALSE)
print(pairwise_wilcox)

    Pairwise comparisons using Wilcoxon rank sum test with continuity correction 

data:  circ_data$ctDNA.Base.MTM and circ_data$cN 

   N0     N1     N2    
N1 0.0473 -      -     
N2 0.0094 0.4108 -     
N3 0.3736 0.9777 0.7580

P value adjustment method: none 
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available == "TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base != "",]
circ_data$cN <- factor(circ_data$cN, levels = c("N0","N1","N2","N3"))
circ_data$ctDNA.Base.MTM <- as.numeric(circ_data$ctDNA.Base.MTM)
cN_levels <- levels(circ_data$cN)
p_value_matrix <- matrix(NA, nrow = length(cN_levels), ncol = length(cN_levels))
rownames(p_value_matrix) <- cN_levels
colnames(p_value_matrix) <- cN_levels

for (i in 1:length(cN_levels)) {
  for (j in i:length(cN_levels)) {
    if (i != j) {
      # Extract data for both groups
      data1 <- circ_data %>% filter(cN == cN_levels[i]) %>% pull(ctDNA.Base.MTM)
      data2 <- circ_data %>% filter(cN == cN_levels[j]) %>% pull(ctDNA.Base.MTM)
      
      # Perform Wilcoxon test and store p-value
      test_result <- wilcox.test(data1, data2, exact = FALSE)
      p_value_matrix[i, j] <- test_result$p.value
      p_value_matrix[j, i] <- test_result$p.value  # Make symmetric
    } else {
      p_value_matrix[i, j] <- 1  # Self-comparison = 1
    }
  }
}

p_value_matrix[is.na(p_value_matrix)] <- 1.00
p_value_data <- melt(p_value_matrix)
colnames(p_value_data) <- c("cN1", "cN2", "p_value")
p_value_data <- p_value_data %>%
  mutate(
    significance = case_when(
      p_value < 0.001 ~ "***",
      p_value < 0.01 ~ "**",
      p_value < 0.05 ~ "*",
      TRUE ~ ""
    )
  )

ggplot(p_value_data, aes(x = cN1, y = cN2, fill = p_value)) +
  geom_tile(color = "white", size = 0.8) +  # Thicker grid lines for separation
  geom_text(aes(label = significance), color = "black", size = 6, fontface = "bold") +  # Significance markers
  scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0.05) +  # Gradient colors
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 12, face = "bold"),
        axis.text.y = element_text(size = 12, face = "bold"),
        panel.grid = element_blank()) +
  labs(title = "Pairwise Wilcoxon-Test P-Values (ctDNA.Base.MTM by cN)",
       x = "Status", y = "cN Status", fill = "P-Value")


rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available == "TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base != "",]
circ_data$cT <- factor(circ_data$cT, levels = c("T0", "T1", "T2", "T3", "T4"))
circ_data$cN <- factor(circ_data$cN, levels = c("N0", "N1", "N2", "N3"))
circ_data$ctDNA.Base.MTM <- as.numeric(circ_data$ctDNA.Base.MTM)

median_ctDNA <- circ_data %>%
  group_by(cT, cN) %>%
  summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE)) %>%
  ungroup()
`summarise()` has grouped output by 'cT'. You can override using the `.groups` argument.
p_value_matrix <- dcast(median_ctDNA, cT ~ cN, value.var = "median_ctDNA_Base_MTM")
p_value_data <- melt(p_value_matrix, id.vars = "cT", variable.name = "cN", value.name = "median_value")
p_value_data$missing <- ifelse(is.na(p_value_data$median_value), "Missing", "Present")
p_value_data$median_value[is.na(p_value_data$median_value)] <- 0

ggplot(p_value_data, aes(x = cN, y = cT, fill = median_value)) +
  geom_tile(color = "black", size = 0.5) +  # Black gridlines for separation
  geom_text(aes(label = round(median_value, 2)), color = "black", size = 5) +  # Display median values
  scale_fill_gradient(low = "white", high = "blue") +  # Color gradient similar to the reference image
  theme_minimal() +
  theme(axis.text.x = element_text(size = 12, face = "bold"),
        axis.text.y = element_text(size = 12, face = "bold"),
        panel.grid = element_blank()) +
  labs(title = "Median ctDNA.Base.MTM by cT and cN",
       x = "cN Status", y = "cT Status", fill = "Median MTM") +
  geom_tile(data = subset(p_value_data, missing == "Missing"), 
            aes(x = cN, y = cT), color = "black", fill = NA, size = 0.5, linetype = "dashed")  # Add diagonal cross for missing values


rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

tally(~cM, data=circ_data, margins = TRUE)
cM
   M0    M1 Total 
   60     2    62 
circ_data$cM <- factor(circ_data$cM, levels = c("M0","M1"), labels = c("M0 (n=60)","M1 (n=2)"))
boxplot(ctDNA.Base.MTM~cM, data=circ_data, main="ctDNA pre-treatment MTM - cM", xlab="cM", ylab="MTM/mL", col="white",border="black", ylim = c(0, 500))

median_ctDNA.cM <- circ_data %>%
  group_by(cM) %>%
  summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.cM)
m4<-wilcox.test(ctDNA.Base.MTM ~ cM, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m4)

    Wilcoxon rank sum test with continuity correction

data:  ctDNA.Base.MTM by cM
W = 53, p-value = 0.7955
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -469.98001   76.11002
sample estimates:
difference in location 
           -0.07005722 
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

tally(~p16.status, data=circ_data, margins = TRUE)
p16.status
Negative Positive    Total 
      23       39       62 
circ_data$p16.status <- factor(circ_data$p16.status, levels = c("Negative","Positive"), labels = c("p16 neg (n=23)","p16 pos (n=39)"))
boxplot(ctDNA.Base.MTM~p16.status, data=circ_data, main="ctDNA pre-treatment MTM - p16 status", xlab="p16 status", ylab="MTM/mL", col="white",border="black", ylim = c(0, 200))

median_ctDNA.p16 <- circ_data %>%
  group_by(p16.status) %>%
  summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.p16)
m5<-wilcox.test(ctDNA.Base.MTM ~ p16.status, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m5)

    Wilcoxon rank sum test with continuity correction

data:  ctDNA.Base.MTM by p16.status
W = 269, p-value = 0.009047
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
 -27.889950  -1.040095
sample estimates:
difference in location 
             -8.739937 
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

tally(~Prim.Location, data=circ_data, margins = TRUE)
Prim.Location
                        Larynx/Hypopharynx                                Oral cavity                                 Oropharynx Other (paranasal sinus and nasopharyngeal) 
                                         3                                          7                                         47                                          5 
                                     Total 
                                        62 
circ_data$Prim.Location <- factor(circ_data$Prim.Location, levels = c("Larynx/Hypopharynx","Oral cavity", "Oropharynx", "Other (paranasal sinus and nasopharyngeal)"))
boxplot(ctDNA.Base.MTM~Prim.Location, data=circ_data, main="ctDNA pre-treatment MTM - Tumor Location", xlab="Tumor Location", ylab="MTM/mL", col="white",border="black", ylim = c(0, 200))

median_ctDNA.loc <- circ_data %>%
  group_by(Prim.Location) %>%
  summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.loc)
pairwise_wilcox <- pairwise.wilcox.test(circ_data$ctDNA.Base.MTM, circ_data$Prim.Location, 
                                        p.adjust.method = "none", 
                                        exact = FALSE)

print(pairwise_wilcox)

    Pairwise comparisons using Wilcoxon rank sum test with continuity correction 

data:  circ_data$ctDNA.Base.MTM and circ_data$Prim.Location 

                                           Larynx/Hypopharynx Oral cavity Oropharynx
Oral cavity                                0.644              -           -         
Oropharynx                                 0.253              0.065       -         
Other (paranasal sinus and nasopharyngeal) 0.551              0.563       0.653     

P value adjustment method: none 
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available == "TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base != "",]
circ_data$Prim.Location <- factor(circ_data$Prim.Location, levels = c("Larynx/Hypopharynx","Oral cavity", "Oropharynx", "Other (paranasal sinus and nasopharyngeal)"), labels = c("LRX/HPRX","OC","PRX","Other"))
circ_data$ctDNA.Base.MTM <- as.numeric(circ_data$ctDNA.Base.MTM)
pl_levels <- levels(circ_data$Prim.Location)
p_value_matrix <- matrix(NA, nrow = length(pl_levels), ncol = length(pl_levels))
rownames(p_value_matrix) <- pl_levels
colnames(p_value_matrix) <- pl_levels

for (i in 1:length(pl_levels)) {
  for (j in i:length(pl_levels)) {
    if (i != j) {
      # Extract data for both groups
      data1 <- circ_data %>% filter(Prim.Location == pl_levels[i]) %>% pull(ctDNA.Base.MTM)
      data2 <- circ_data %>% filter(Prim.Location == pl_levels[j]) %>% pull(ctDNA.Base.MTM)
      
      # Perform Wilcoxon test and store p-value
      test_result <- wilcox.test(data1, data2, exact = FALSE)
      p_value_matrix[i, j] <- test_result$p.value
      p_value_matrix[j, i] <- test_result$p.value  # Make symmetric
    } else {
      p_value_matrix[i, j] <- 1  # Self-comparison = 1
    }
  }
}

p_value_matrix[is.na(p_value_matrix)] <- 1.00
p_value_data <- melt(p_value_matrix)
colnames(p_value_data) <- c("pl1", "pl2", "p_value")
p_value_data <- p_value_data %>%
  mutate(
    significance = case_when(
      p_value < 0.001 ~ "***",
      p_value < 0.01 ~ "**",
      p_value < 0.05 ~ "*",
      TRUE ~ ""
    )
  )

ggplot(p_value_data, aes(x = pl1, y = pl2, fill = p_value)) +
  geom_tile(color = "white", size = 0.8) +  # Thicker grid lines for separation
  geom_text(aes(label = significance), color = "black", size = 6, fontface = "bold") +  # Significance markers
  scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0.05) +  # Gradient colors
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 12, face = "bold"),
        axis.text.y = element_text(size = 12, face = "bold"),
        panel.grid = element_blank()) +
  labs(title = "Pairwise Wilcoxon-Test P-Values (ctDNA.Base.MTM by Tumor Location)",
       x = "Tumor Location", y = "Tumor Location", fill = "P-Value")

#PFS by ctDNA status at MRD

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.MRD, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) ~ 
    ctDNA.MRD, data = circ_data)

                    n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 56      8     NA      NA      NA
ctDNA.MRD=POSITIVE 13      8   15.5    4.21      NA
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at MRD", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")

summary(KM_curve, times= c(0, 12, 24, 36))
Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.MRD=NEGATIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0     56       0    1.000  0.0000        1.000        1.000
   12     44       5    0.910  0.0384        0.797        0.962
   24     27       3    0.841  0.0523        0.705        0.918
   36      8       0    0.841  0.0523        0.705        0.918

                ctDNA.MRD=POSITIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0     13       0    1.000   0.000       1.0000        1.000
   12      6       6    0.538   0.138       0.2477        0.760
   24      2       2    0.323   0.144       0.0862        0.594
   36      2       0    0.323   0.144       0.0862        0.594
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)

  n= 69, number of events= 16 

                    coef exp(coef) se(coef)     z Pr(>|z|)    
ctDNA.MRDPOSITIVE 1.8545    6.3886   0.5021 3.693 0.000221 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                  exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE     6.389     0.1565     2.388     17.09

Concordance= 0.69  (se = 0.059 )
Likelihood ratio test= 12.07  on 1 df,   p=5e-04
Wald test            = 13.64  on 1 df,   p=2e-04
Score (logrank) test = 17.94  on 1 df,   p=2e-05
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 6.39 (2.39-17.09); p = 0"
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
G2;H2;Warningh in stats::chisq.test(x, y, ...) :
  Chi-squared approximation may be incorrectg
print(chi_square_test)

    Pearson's Chi-squared test with Yates' continuity correction

data:  contingency_table
X-squared = 10.706, df = 1, p-value = 0.001068
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 0.001047
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
  2.06321 46.02731
sample estimates:
odds ratio 
  9.153574 
print(contingency_table)
          
           No Progression Progression
  Negative             48           8
  Positive              5           8
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at MRD", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size

#OS by ctDNA status at MRD

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.MRD, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$OS.months, event = circ_data$OS.Event) ~ 
    ctDNA.MRD, data = circ_data)

                    n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 56      1     NA      NA      NA
ctDNA.MRD=POSITIVE 13      5     NA    12.3      NA
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA at MRD", ylab= "Overall Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")

summary(KM_curve, times= c(12, 24, 36))
Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.MRD=NEGATIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     48       1    0.982  0.0177         0.88        0.997
   24     30       0    0.982  0.0177         0.88        0.997
   36     10       0    0.982  0.0177         0.88        0.997

                ctDNA.MRD=POSITIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      8       3    0.769   0.117        0.442        0.919
   24      4       2    0.561   0.153        0.233        0.795
   36      3       0    0.561   0.153        0.233        0.795
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)

  n= 69, number of events= 6 

                    coef exp(coef) se(coef)     z Pr(>|z|)   
ctDNA.MRDPOSITIVE  3.247    25.718    1.096 2.962  0.00306 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                  exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE     25.72    0.03888         3     220.5

Concordance= 0.832  (se = 0.081 )
Likelihood ratio test= 13.07  on 1 df,   p=3e-04
Wald test            = 8.77  on 1 df,   p=0.003
Score (logrank) test = 19.67  on 1 df,   p=9e-06
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 25.72 (3-220.48); p = 0.003"
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)
G2;H2;Warningh in stats::chisq.test(x, y, ...) :
  Chi-squared approximation may be incorrectg
print(chi_square_test)

    Pearson's Chi-squared test with Yates' continuity correction

data:  contingency_table
X-squared = 13.554, df = 1, p-value = 0.0002318
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 0.0006155
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
    3.015475 1634.641331
sample estimates:
odds ratio 
  31.44433 
print(contingency_table)
          
           Alive Deceased
  Negative    55        1
  Positive     8        5
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at MRD", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Living Status",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size

#PFS by ctDNA status at MRD - exclude pts with adjuvant treatment post-MRD

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
excluded_ids <- c("UNM-007", "UNM-008", "UNM-023", "UNM-027", "UNM-029", 
                  "UNM-030", "UNM-035", "UNM-045", "UNM-051", "UNM-059", 
                  "UNM-075", "UNM-082", "UNM-032", "UNM-042", "UNM-043", 
                  "UNM-048", "UNM-050", "UNM-070")
circ_data <- circ_data[!circ_data$PatientID %in% excluded_ids, ]

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.MRD, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) ~ 
    ctDNA.MRD, data = circ_data)

                    n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 44      6     NA      NA      NA
ctDNA.MRD=POSITIVE  7      6   11.3    3.12      NA
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at MRD", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")

summary(KM_curve, times= c(0, 12, 24, 36))
Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.MRD=NEGATIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0     44       0    1.000  0.0000        1.000        1.000
   12     37       3    0.932  0.0380        0.803        0.977
   24     21       3    0.847  0.0582        0.688        0.929
   36      7       0    0.847  0.0582        0.688        0.929

                ctDNA.MRD=POSITIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0      7       0    1.000   0.000      1.00000        1.000
   12      3       4    0.429   0.187      0.09775        0.734
   24      1       2    0.143   0.132      0.00712        0.465
   36      1       0    0.143   0.132      0.00712        0.465
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)

  n= 51, number of events= 12 

                     coef exp(coef) se(coef)     z Pr(>|z|)    
ctDNA.MRDPOSITIVE  2.3109   10.0834   0.5805 3.981 6.87e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                  exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE     10.08    0.09917     3.232     31.46

Concordance= 0.711  (se = 0.067 )
Likelihood ratio test= 13.27  on 1 df,   p=3e-04
Wald test            = 15.85  on 1 df,   p=7e-05
Score (logrank) test = 24.07  on 1 df,   p=9e-07
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 10.08 (3.23-31.46); p = 0"
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
G2;H2;Warningh in stats::chisq.test(x, y, ...) :
  Chi-squared approximation may be incorrectg
print(chi_square_test)

    Pearson's Chi-squared test with Yates' continuity correction

data:  contingency_table
X-squared = 13.662, df = 1, p-value = 0.0002189
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 0.0003181
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
    3.315922 1768.464983
sample estimates:
odds ratio 
  33.80814 
print(contingency_table)
          
           No Progression Progression
  Negative             38           6
  Positive              1           6
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at MRD", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size

#OS by ctDNA status at MRD - exclude pts with adjuvant treatment post-MRD

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
excluded_ids <- c("UNM-007", "UNM-008", "UNM-023", "UNM-027", "UNM-029", 
                  "UNM-030", "UNM-035", "UNM-045", "UNM-051", "UNM-059", 
                  "UNM-075", "UNM-082", "UNM-032", "UNM-042", "UNM-043", 
                  "UNM-048", "UNM-050", "UNM-070")
circ_data <- circ_data[!circ_data$PatientID %in% excluded_ids, ]

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.MRD, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$OS.months, event = circ_data$OS.Event) ~ 
    ctDNA.MRD, data = circ_data)

                    n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 44      1     NA      NA      NA
ctDNA.MRD=POSITIVE  7      3     NA    12.3      NA
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA at MRD", ylab= "Overall Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")

summary(KM_curve, times= c(12, 24, 36))
Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.MRD=NEGATIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     39       1    0.977  0.0225        0.849        0.997
   24     23       0    0.977  0.0225        0.849        0.997
   36      8       0    0.977  0.0225        0.849        0.997

                ctDNA.MRD=POSITIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      5       1    0.857   0.132        0.334        0.979
   24      3       2    0.514   0.204        0.118        0.813
   36      2       0    0.514   0.204        0.118        0.813
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)

  n= 51, number of events= 4 

                    coef exp(coef) se(coef)     z Pr(>|z|)   
ctDNA.MRDPOSITIVE  3.026    20.613    1.155 2.619  0.00881 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                  exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE     20.61    0.04851     2.142     198.4

Concordance= 0.799  (se = 0.119 )
Likelihood ratio test= 8.14  on 1 df,   p=0.004
Wald test            = 6.86  on 1 df,   p=0.009
Score (logrank) test = 13.95  on 1 df,   p=2e-04
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 20.61 (2.14-198.38); p = 0.009"
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)
G2;H2;Warningh in stats::chisq.test(x, y, ...) :
  Chi-squared approximation may be incorrectg
print(chi_square_test)

    Pearson's Chi-squared test with Yates' continuity correction

data:  contingency_table
X-squared = 8.7198, df = 1, p-value = 0.003148
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 0.006303
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
    1.805221 1704.546058
sample estimates:
odds ratio 
  27.80596 
print(contingency_table)
          
           Alive Deceased
  Negative    43        1
  Positive     4        3
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at MRD", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Living Status",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size

#PFS by ctDNA status at MRD Stage I/II

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$cStage=="I/II",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.MRD, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) ~ 
    ctDNA.MRD, data = circ_data)

                    n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 29      2     NA      NA      NA
ctDNA.MRD=POSITIVE  5      2     NA    6.01      NA
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at MRD Stage I/II", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")

summary(KM_curve, times= c(0, 12, 24, 36))
Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.MRD=NEGATIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0     29       0    1.000  0.0000        1.000        1.000
   12     25       1    0.966  0.0339        0.779        0.995
   24     16       1    0.925  0.0510        0.732        0.981
   36      3       0    0.925  0.0510        0.732        0.981

                ctDNA.MRD=POSITIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0      5       0      1.0   0.000        1.000        1.000
   12      2       2      0.6   0.219        0.126        0.882
   24      1       0      0.6   0.219        0.126        0.882
   36      1       0      0.6   0.219        0.126        0.882
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)

  n= 34, number of events= 4 

                   coef exp(coef) se(coef)     z Pr(>|z|)  
ctDNA.MRDPOSITIVE 2.286     9.838    1.035 2.209   0.0272 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                  exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE     9.838     0.1016     1.294     74.78

Concordance= 0.725  (se = 0.118 )
Likelihood ratio test= 4.21  on 1 df,   p=0.04
Wald test            = 4.88  on 1 df,   p=0.03
Score (logrank) test = 7.19  on 1 df,   p=0.007
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 9.84 (1.29-74.78); p = 0.027"
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
G2;H2;Warningh in stats::chisq.test(x, y, ...) :
  Chi-squared approximation may be incorrectg
print(chi_square_test)

    Pearson's Chi-squared test with Yates' continuity correction

data:  contingency_table
X-squared = 1.8778, df = 1, p-value = 0.1706
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 0.09391
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
   0.4412663 153.9655852
sample estimates:
odds ratio 
  8.070894 
print(contingency_table)
          
           No Progression Progression
  Negative             27           2
  Positive              3           2
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at MRD Stage I/II", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size

#PFS by ctDNA status at MRD Stage III/IV

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$cStage=="III/IV",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.MRD, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) ~ 
    ctDNA.MRD, data = circ_data)

                    n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 27      6     NA      NA      NA
ctDNA.MRD=POSITIVE  8      6   13.4    4.21      NA
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at MRD Stage III/IV", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")

summary(KM_curve, times= c(0, 12, 24, 36))
Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.MRD=NEGATIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0     27       0    1.000  0.0000        1.000        1.000
   12     19       4    0.850  0.0691        0.649        0.941
   24     11       2    0.753  0.0892        0.526        0.882
   36      5       0    0.753  0.0892        0.526        0.882

                ctDNA.MRD=POSITIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0      8       0     1.00   0.000       1.0000        1.000
   12      4       4     0.50   0.177       0.1520        0.775
   24      1       2     0.25   0.153       0.0371        0.558
   36      1       0     0.25   0.153       0.0371        0.558
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)

  n= 35, number of events= 12 

                    coef exp(coef) se(coef)     z Pr(>|z|)  
ctDNA.MRDPOSITIVE 1.4899    4.4365   0.5788 2.574     0.01 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                  exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE     4.437     0.2254     1.427     13.79

Concordance= 0.663  (se = 0.07 )
Likelihood ratio test= 6.1  on 1 df,   p=0.01
Wald test            = 6.63  on 1 df,   p=0.01
Score (logrank) test = 7.93  on 1 df,   p=0.005
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 4.44 (1.43-13.79); p = 0.01"
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
G2;H2;Warningh in stats::chisq.test(x, y, ...) :
  Chi-squared approximation may be incorrectg
print(chi_square_test)

    Pearson's Chi-squared test with Yates' continuity correction

data:  contingency_table
X-squared = 5.4671, df = 1, p-value = 0.01938
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 0.01073
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
   1.308188 121.976548
sample estimates:
odds ratio 
  9.642373 
print(contingency_table)
          
           No Progression Progression
  Negative             21           6
  Positive              2           6
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at MRD Stage I/II", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size

#PFS by ctDNA at MRD p16(+)

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$p16.status=="Positive",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.MRD, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) ~ 
    ctDNA.MRD, data = circ_data)

                    n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 29      2     NA      NA      NA
ctDNA.MRD=POSITIVE  8      4   22.2    6.01      NA
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at MRD p16(+)", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")

summary(KM_curve, times= c(0, 12, 24, 36))
Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.MRD=NEGATIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0     29       0    1.000  0.0000        1.000        1.000
   12     23       1    0.966  0.0339        0.779        0.995
   24     15       1    0.920  0.0553        0.711        0.980
   36      4       0    0.920  0.0553        0.711        0.980

                ctDNA.MRD=POSITIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0      8       0    1.000   0.000        1.000        1.000
   12      4       3    0.625   0.171        0.229        0.861
   24      2       1    0.417   0.205        0.072        0.747
   36      2       0    0.417   0.205        0.072        0.747
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)

  n= 37, number of events= 6 

                     coef exp(coef) se(coef)     z Pr(>|z|)   
ctDNA.MRDPOSITIVE  2.3218   10.1936   0.8706 2.667  0.00766 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                  exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE     10.19     0.0981      1.85     56.16

Concordance= 0.767  (se = 0.09 )
Likelihood ratio test= 7.47  on 1 df,   p=0.006
Wald test            = 7.11  on 1 df,   p=0.008
Score (logrank) test = 10.81  on 1 df,   p=0.001
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 10.19 (1.85-56.16); p = 0.008"
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
G2;H2;Warningh in stats::chisq.test(x, y, ...) :
  Chi-squared approximation may be incorrectg
print(chi_square_test)

    Pearson's Chi-squared test with Yates' continuity correction

data:  contingency_table
X-squared = 5.6953, df = 1, p-value = 0.01701
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 0.01294
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
   1.281882 176.017338
sample estimates:
odds ratio 
  12.07276 
print(contingency_table)
          
           No Progression Progression
  Negative             27           2
  Positive              4           4
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at MRD p16(+)", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size

#PFS by ctDNA at MRD p16(-)

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$p16.status=="Negative",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.MRD, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) ~ 
    ctDNA.MRD, data = circ_data)

                    n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 27      6     NA      NA      NA
ctDNA.MRD=POSITIVE  5      4   11.3    4.21      NA
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at MRD p16(-)", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")

summary(KM_curve, times= c(0, 12, 24, 36))
Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.MRD=NEGATIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0     27       0    1.000  0.0000        1.000        1.000
   12     21       4    0.850  0.0691        0.649        0.941
   24     12       2    0.762  0.0858        0.542        0.886
   36      4       0    0.762  0.0858        0.542        0.886

                ctDNA.MRD=POSITIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0      5       0      1.0   0.000        1.000        1.000
   12      2       3      0.4   0.219        0.052        0.753
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)

  n= 32, number of events= 10 

                    coef exp(coef) se(coef)     z Pr(>|z|)   
ctDNA.MRDPOSITIVE 1.7286    5.6328   0.6515 2.653  0.00797 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                  exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE     5.633     0.1775     1.571      20.2

Concordance= 0.655  (se = 0.072 )
Likelihood ratio test= 5.79  on 1 df,   p=0.02
Wald test            = 7.04  on 1 df,   p=0.008
Score (logrank) test = 8.93  on 1 df,   p=0.003
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 5.63 (1.57-20.2); p = 0.008"
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
G2;H2;Warningh in stats::chisq.test(x, y, ...) :
  Chi-squared approximation may be incorrectg
print(chi_square_test)

    Pearson's Chi-squared test with Yates' continuity correction

data:  contingency_table
X-squared = 4.1417, df = 1, p-value = 0.04184
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 0.02419
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
   1.013292 717.346329
sample estimates:
odds ratio 
  12.61534 
print(contingency_table)
          
           No Progression Progression
  Negative             21           6
  Positive              1           4
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at MRD p16(-)", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size

#PFS by ctDNA status at surveillance

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.Surveillance, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) ~ 
    ctDNA.Surveillance, data = circ_data)

                             n events median 0.95LCL 0.95UCL
ctDNA.Surveillance=NEGATIVE 51      3     NA      NA      NA
ctDNA.Surveillance=POSITIVE 17     14   14.7    11.3    24.8
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at Surveillance", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")

summary(KM_curve, times= c(0, 12, 24, 36))
Call: survfit(formula = surv_object ~ ctDNA.Surveillance, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.Surveillance=NEGATIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0     51       0    1.000  0.0000        1.000        1.000
   12     40       2    0.961  0.0272        0.852        0.990
   24     22       1    0.929  0.0410        0.788        0.977
   36      8       0    0.929  0.0410        0.788        0.977

                ctDNA.Surveillance=POSITIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0     17       0    1.000  0.0000       1.0000        1.000
   12     10       7    0.588  0.1194       0.3254        0.778
   24      5       5    0.294  0.1105       0.1071        0.511
   36      1       2    0.176  0.0925       0.0435        0.383
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data) 
ggforest(cox_fit,data = circ_data) 

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)

  n= 68, number of events= 17 

                              coef exp(coef) se(coef)     z Pr(>|z|)    
ctDNA.SurveillancePOSITIVE  2.9034   18.2359   0.6376 4.554 5.27e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                           exp(coef) exp(-coef) lower .95 upper .95
ctDNA.SurveillancePOSITIVE     18.24    0.05484     5.227     63.62

Concordance= 0.802  (se = 0.052 )
Likelihood ratio test= 29.88  on 1 df,   p=5e-08
Wald test            = 20.74  on 1 df,   p=5e-06
Score (logrank) test = 39.64  on 1 df,   p=3e-10
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 18.24 (5.23-63.62); p = 0"
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
G2;H2;Warningh in stats::chisq.test(x, y, ...) :
  Chi-squared approximation may be incorrectg
print(chi_square_test)

    Pearson's Chi-squared test with Yates' continuity correction

data:  contingency_table
X-squared = 35.791, df = 1, p-value = 2.197e-09
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 3.189e-09
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
  11.08872 588.54005
sample estimates:
odds ratio 
  64.48451 
print(contingency_table)
          
           No Progression Progression
  Negative             48           3
  Positive              3          14
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at Surveillance", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size

#Median numbers of time points and lead time in the longitudinal setting

# Load the dataset
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)

median_Nsurvtps <- median(circ_datadf$Nsurvtps, na.rm = TRUE)
min_Nsurvtps <- min(circ_datadf$Nsurvtps, na.rm = TRUE)
max_Nsurvtps <- max(circ_datadf$Nsurvtps, na.rm = TRUE)

cat(sprintf("Median # of surveillance time points: %d (%d-%d)\n", 
            median_Nsurvtps, min_Nsurvtps, max_Nsurvtps))
Median # of surveillance time points: 4 (1-13)
circ_datadf$LeadTime_Months <- circ_datadf$LeadTime / 30.437
median_LeadTime <- median(circ_datadf$LeadTime_Months, na.rm = TRUE)
min_LeadTime <- min(circ_datadf$LeadTime_Months, na.rm = TRUE)
max_LeadTime <- max(circ_datadf$LeadTime_Months, na.rm = TRUE)
cat(sprintf("Longitudinally, ctDNA positivity preceded progression by a median of %.2f mo (%.2f–%.2f)\n", 
            median_LeadTime, min_LeadTime, max_LeadTime))
Longitudinally, ctDNA positivity preceded progression by a median of 4.44 mo (0.00–13.96)

#Time-dependent analysis for PFS in longitudinal time points

rm(list=ls())
setwd("~/Downloads")
dt <- read_xlsx("CLIA HNSCC Peddada Clinical Data_Time dependent.xlsx") |>
  clean_names() |>
  mutate(across(.cols = c(window_start_date,dfs_date,
                          surveillance_1_date:surveillance_12_date), 
                .fns = ~ as_date(as.Date(.x, format = "%Y-%m-%d"))))
G2;H2;Warningh: Expecting numeric in Z5 / R5C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z8 / R8C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z9 / R9C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z15 / R15C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z17 / R17C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z18 / R18C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z19 / R19C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z22 / R22C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z23 / R23C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z25 / R25C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z26 / R26C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z27 / R27C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z28 / R28C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z29 / R29C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z32 / R32C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z34 / R34C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z35 / R35C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z38 / R38C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z41 / R41C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z47 / R47C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z48 / R48C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z49 / R49C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z56 / R56C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z58 / R58C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z60 / R60C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z63 / R63C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z64 / R64C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z65 / R65C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z67 / R67C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z68 / R68C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z71 / R71C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z72 / R72C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z74 / R74C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z78 / R78C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z81 / R81C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z82 / R82C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z83 / R83C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z86 / R86C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z87 / R87C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z89 / R89C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z90 / R90C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z93 / R93C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z95 / R95C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z96 / R96C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z98 / R98C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z99 / R99C26: got a dateg
dt_biomarker <- dt |>
  select(pts_id, ct_dna_surveillance_available,
         window_start_date,
         surveillance_1_status:surveillance_12_date) |>
  filter(ct_dna_surveillance_available) |>
  pivot_longer(cols = surveillance_1_status:surveillance_12_date,
               names_to = c("visit_number", ".value"),
               names_pattern = "surveillance_(.)_(.*)") |>
  mutate(biomarker_time = day(days(date - window_start_date))) |>
  select(pts_id, biomarker_time, biomarker_status = status) |>
  filter(!is.na(biomarker_time))

glimpse(dt_biomarker)
Rows: 219
Columns: 3
$ pts_id           <chr> "UNM-004", "UNM-004", "UNM-004", "UNM-008", "UNM-008", "UNM-008", "UNM-008", "UNM-008", "UNM-009", "UNM-009", "UNM-009", "UNM-009", "UNM-009", "UNM-014", "UNM-016"…
$ biomarker_time   <dbl> 18, 25719, 179, -75, 25647, 154, 236, 322, 46, 25792, 327, 418, 507, 156, 112, 25865, 387, 481, 9, 25746, 265, 28, 477, 19, 25756, 273, 361, 454, 550, 649, 32, 257…
$ biomarker_status <chr> "NEGATIVE", "POSITIVE", "POSITIVE", "NEGATIVE", "NEGATIVE", "NEGATIVE", "NEGATIVE", NA, "NEGATIVE", "NEGATIVE", "NEGATIVE", "NEGATIVE", "NEGATIVE", "NEGATIVE", "NE…
dt_survival <- dt |>
  select(pts_id, ct_dna_surveillance_available,
         window_start_date:dfs_date, dfs_event) |>  # Added dfs_event here
  filter(ct_dna_surveillance_available) |>
  mutate(dfs_time = (dfs_date - window_start_date),
         dfs_time = day(days(dfs_time)),
         dfs_event = as.numeric(dfs_event)) |>
  select(pts_id, dfs_time, dfs_event)

glimpse(dt_survival)
Rows: 68
Columns: 3
$ pts_id    <chr> "UNM-004", "UNM-008", "UNM-009", "UNM-014", "UNM-016", "UNM-018", "UNM-019", "UNM-020", "UNM-021", "UNM-023", "UNM-024", "UNM-025", "UNM-026", "UNM-027", "UNM-028", "UNM-…
$ dfs_time  <dbl> 308, 953, 513, 208, 655, 1058, 1065, 897, 80, 237, 535, 880, 638, 934, 1324, 989, 437, 113, 566, 647, 535, 943, 1069, 108, 591, 150, 305, 283, 126, 918, 192, 411, 366, 10…
$ dfs_event <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0,…
aux <- dt_survival %>% 
  filter(dfs_time <= 0)

tab <- left_join(aux, dt) |>
  select(pts_id, window_start_date, dfs_time, dfs_date,
         surveillance_1_date:surveillance_12_date) |>
  mutate(across(.cols = dfs_date:surveillance_12_date, 
                .fns = ~ as_date(.x))) |>
  select(pts_id, window_start_date, dfs_date, dfs_time)
Joining with `by = join_by(pts_id, dfs_event)`
datatable(tab, filter = "top")

dt_survival <- dt_survival |>
  filter(dfs_time > 0)

aux <- dt |>
  select(pts_id, ct_dna_surveillance_available,
         window_start_date, dfs_date,
         surveillance_1_date:surveillance_12_date) |>
  mutate(across(.cols = surveillance_1_date:surveillance_12_date, 
                .fns = ~ .x - window_start_date)) |>
  mutate(across(.cols = surveillance_1_date:surveillance_12_date, 
                .fns = ~ .x < 0)) |>
  rowwise() |>
  mutate(sum_neg = 
           sum(c_across(surveillance_1_date:surveillance_12_date),
               na.rm = TRUE))  |>
  select(pts_id, sum_neg)

tab <- left_join(aux, dt) |>
  filter(sum_neg > 0) |>
  select(pts_id, sum_neg, window_start_date,
         surveillance_1_date:surveillance_12_date) |>
  mutate(across(.cols = window_start_date:surveillance_12_date, 
                .fns = ~ as_date(.x))) 
Joining with `by = join_by(pts_id)`
G2;H2;Warningh in left_join(aux, dt) :
  Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 99 of `x` matches multiple rows in `y`.
ℹ Row 99 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship = "many-to-many"` to silence this warning.g
datatable(tab, filter = "top")

aux <- dt |>
  select(pts_id, ct_dna_surveillance_available,
         window_start_date, dfs_date,
         surveillance_1_date:surveillance_12_date) |>
  mutate(across(.cols = dfs_date:surveillance_12_date, 
                .fns = ~ .x - window_start_date)) |>
  mutate(across(.cols = surveillance_2_date:surveillance_12_date,
                .fns = ~ dfs_date < .x)) |>
  rowwise() |>
  mutate(n_biomarker_after_event = sum(c_across(surveillance_2_date:
                                                  surveillance_12_date), 
                                       na.rm = TRUE)) |>
  mutate(across(.cols = surveillance_1_date:surveillance_12_date,
                .fns = ~ !is.na(.x))) |>
  mutate(total_biomarker = sum(c_across(surveillance_2_date:
                                          surveillance_12_date), 
                               na.rm = TRUE)) |>
  select(pts_id, n_biomarker_after_event, total_biomarker)

temp <- aux |> 
  select(-pts_id) |> 
  group_by(n_biomarker_after_event, total_biomarker) |>  # Direct grouping
  summarise(freq = n(), .groups = "drop")  # Drop groups after summarization


tab <- left_join(aux, dt) |>
  select(pts_id, n_biomarker_after_event, total_biomarker, 
         dfs_date,
         surveillance_2_date:surveillance_12_date) |>
  mutate(across(.cols = dfs_date:surveillance_12_date, 
                .fns = ~ as_date(.x))) |>
  filter(n_biomarker_after_event > 0)
Joining with `by = join_by(pts_id)`
G2;H2;Warningh in left_join(aux, dt) :
  Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 99 of `x` matches multiple rows in `y`.
ℹ Row 99 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship = "many-to-many"` to silence this warning.g
datatable(tab, filter = "top")

aux <- tmerge(data1 = dt_survival, 
              data2 = dt_survival,
              id = pts_id, 
              dfs_event = event(dfs_time, dfs_event))
dt_final <- tmerge(data1 = aux, 
                   data2 = dt_biomarker,
                   id = pts_id, 
                   biomarker_status = 
                     tdc(biomarker_time, biomarker_status))

datatable(dt_final, filter = "top")

# Syntax if there is not time-dependent covariate
# fit <- coxph(Surv(dfs_time, dfs_event) ~ biomarker_status,
#              data = dt_final)
# summary(fit)

fit <- coxph(Surv(tstart, tstop, dfs_event) ~ biomarker_status,
             data = dt_final)
summary(fit)
Call:
coxph(formula = Surv(tstart, tstop, dfs_event) ~ biomarker_status, 
    data = dt_final)

  n= 169, number of events= 17 
   (66 observations deleted due to missingness)

                            coef exp(coef) se(coef)     z Pr(>|z|)    
biomarker_statusPOSITIVE  3.0934   22.0511   0.5178 5.974 2.32e-09 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                         exp(coef) exp(-coef) lower .95 upper .95
biomarker_statusPOSITIVE     22.05    0.04535     7.992     60.84

Concordance= 0.718  (se = 0.06 )
Likelihood ratio test= 30.03  on 1 df,   p=4e-08
Wald test            = 35.68  on 1 df,   p=2e-09
Score (logrank) test = 69.77  on 1 df,   p=<2e-16
cox_fit_summary <- summary(fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 22.05 (7.99-60.84); p = 0"

#PFS by ctDNA status at surveillance Stage I/II

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$cStage=="I/II",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.Surveillance, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) ~ 
    ctDNA.Surveillance, data = circ_data)

                             n events median 0.95LCL 0.95UCL
ctDNA.Surveillance=NEGATIVE 31      0     NA      NA      NA
ctDNA.Surveillance=POSITIVE  6      4   19.6    8.18      NA
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = TRUE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at Surveillance Stage I/II", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")

summary(KM_curve, times= c(0, 12, 24, 36))
Call: survfit(formula = surv_object ~ ctDNA.Surveillance, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.Surveillance=NEGATIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0     31       0        1       0            1            1
   12     25       0        1       0           NA           NA
   24     15       0        1       0           NA           NA
   36      5       0        1       0           NA           NA

                ctDNA.Surveillance=POSITIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0      6       0    1.000   0.000        1.000        1.000
   12      4       2    0.667   0.192        0.195        0.904
   24      3       1    0.500   0.204        0.111        0.804
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxphf(surv_object ~ ctDNA.Surveillance, data=circ_data) 
summary(cox_fit)
coxphf(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)

Model fitted by Penalized ML
Confidence intervals and p-values by Profile Likelihood 

                               coef se(coef) exp(coef) lower 0.95 upper 0.95    Chisq            p
ctDNA.SurveillancePOSITIVE 3.907704 1.668013  49.78452   5.304948   6600.525 13.77645 0.0002059016

Likelihood ratio test=13.77645 on 1 df, p=0.0002059016, n=37
Wald test = 5.488381 on 1 df, p = 0.01914326

Covariance-Matrix:
                           ctDNA.SurveillancePOSITIVE
ctDNA.SurveillancePOSITIVE                   2.782269
cox_fit_summary <- summary(cox_fit)
coxphf(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)

Model fitted by Penalized ML
Confidence intervals and p-values by Profile Likelihood 

                               coef se(coef) exp(coef) lower 0.95 upper 0.95    Chisq            p
ctDNA.SurveillancePOSITIVE 3.907704 1.668013  49.78452   5.304948   6600.525 13.77645 0.0002059016

Likelihood ratio test=13.77645 on 1 df, p=0.0002059016, n=37
Wald test = 5.488381 on 1 df, p = 0.01914326

Covariance-Matrix:
                           ctDNA.SurveillancePOSITIVE
ctDNA.SurveillancePOSITIVE                   2.782269
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
G2;H2;Warningh in stats::chisq.test(x, y, ...) :
  Chi-squared approximation may be incorrectg
print(chi_square_test)

    Pearson's Chi-squared test with Yates' continuity correction

data:  contingency_table
X-squared = 16.773, df = 1, p-value = 4.212e-05
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 0.0002271
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 5.305224      Inf
sample estimates:
odds ratio 
       Inf 
print(contingency_table)
          
           No Progression Progression
  Negative             31           0
  Positive              2           4
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at Surveillance Stage I/II", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size

#PFS by ctDNA status at surveillance Stage III/IV

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$cStage=="III/IV",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.Surveillance, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) ~ 
    ctDNA.Surveillance, data = circ_data)

                             n events median 0.95LCL 0.95UCL
ctDNA.Surveillance=NEGATIVE 20      3     NA      NA      NA
ctDNA.Surveillance=POSITIVE 11     10   12.4    11.3      NA
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at Surveillance Stage III/IV", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")

summary(KM_curve, times= c(0, 12, 24, 36))
Call: survfit(formula = surv_object ~ ctDNA.Surveillance, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.Surveillance=NEGATIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0     20       0    1.000  0.0000        1.000        1.000
   12     15       2    0.900  0.0671        0.656        0.974
   24      7       1    0.825  0.0945        0.539        0.942
   36      3       0    0.825  0.0945        0.539        0.942

                ctDNA.Surveillance=POSITIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0     11       0   1.0000  0.0000      1.00000        1.000
   12      6       5   0.5455  0.1501      0.22854        0.780
   24      2       4   0.1818  0.1163      0.02854        0.442
   36      1       1   0.0909  0.0867      0.00537        0.333
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data) 
ggforest(cox_fit,data = circ_data) 

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)

  n= 31, number of events= 13 

                             coef exp(coef) se(coef)     z Pr(>|z|)   
ctDNA.SurveillancePOSITIVE 2.0850    8.0447   0.6627 3.146  0.00165 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                           exp(coef) exp(-coef) lower .95 upper .95
ctDNA.SurveillancePOSITIVE     8.045     0.1243     2.195     29.48

Concordance= 0.722  (se = 0.07 )
Likelihood ratio test= 12.38  on 1 df,   p=4e-04
Wald test            = 9.9  on 1 df,   p=0.002
Score (logrank) test = 13.83  on 1 df,   p=2e-04
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 8.04 (2.2-29.48); p = 0.002"
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
G2;H2;Warningh in stats::chisq.test(x, y, ...) :
  Chi-squared approximation may be incorrectg
print(chi_square_test)

    Pearson's Chi-squared test with Yates' continuity correction

data:  contingency_table
X-squared = 13.821, df = 1, p-value = 0.000201
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 6.172e-05
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
    4.285476 2542.711782
sample estimates:
odds ratio 
  45.74172 
print(contingency_table)
          
           No Progression Progression
  Negative             17           3
  Positive              1          10
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at Surveillance Stage III/IV", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size

#PFS by ctDNA status at surveillance p16(+)

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$p16.status=="Positive",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.Surveillance, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) ~ 
    ctDNA.Surveillance, data = circ_data)

                             n events median 0.95LCL 0.95UCL
ctDNA.Surveillance=NEGATIVE 30      0     NA      NA      NA
ctDNA.Surveillance=POSITIVE  4      3   19.6    8.18      NA
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = TRUE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at Surveillance p16(+)", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")

summary(KM_curve, times= c(0, 12, 24, 36))
Call: survfit(formula = surv_object ~ ctDNA.Surveillance, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.Surveillance=NEGATIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0     30       0        1       0            1            1
   12     22       0        1       0           NA           NA
   24     13       0        1       0           NA           NA
   36      5       0        1       0           NA           NA

                ctDNA.Surveillance=POSITIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0      4       0     1.00   0.000       1.0000        1.000
   12      3       1     0.75   0.217       0.1279        0.961
   24      2       1     0.50   0.250       0.0578        0.845
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxphf(surv_object ~ ctDNA.Surveillance, data=circ_data) 
summary(cox_fit)
coxphf(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)

Model fitted by Penalized ML
Confidence intervals and p-values by Profile Likelihood 

                               coef se(coef) exp(coef) lower 0.95 upper 0.95    Chisq            p
ctDNA.SurveillancePOSITIVE 3.859906 1.746986  47.46087   4.594352    6384.72 11.46564 0.0007089475

Likelihood ratio test=11.46564 on 1 df, p=0.0007089475, n=34
Wald test = 4.881741 on 1 df, p = 0.02714223

Covariance-Matrix:
                           ctDNA.SurveillancePOSITIVE
ctDNA.SurveillancePOSITIVE                   3.051959
cox_fit_summary <- summary(cox_fit)
coxphf(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)

Model fitted by Penalized ML
Confidence intervals and p-values by Profile Likelihood 

                               coef se(coef) exp(coef) lower 0.95 upper 0.95    Chisq            p
ctDNA.SurveillancePOSITIVE 3.859906 1.746986  47.46087   4.594352    6384.72 11.46564 0.0007089475

Likelihood ratio test=11.46564 on 1 df, p=0.0007089475, n=34
Wald test = 4.881741 on 1 df, p = 0.02714223

Covariance-Matrix:
                           ctDNA.SurveillancePOSITIVE
ctDNA.SurveillancePOSITIVE                   3.051959
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
G2;H2;Warningh in stats::chisq.test(x, y, ...) :
  Chi-squared approximation may be incorrectg
print(chi_square_test)

    Pearson's Chi-squared test with Yates' continuity correction

data:  contingency_table
X-squared = 16.235, df = 1, p-value = 5.594e-05
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 0.0006684
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 4.703171      Inf
sample estimates:
odds ratio 
       Inf 
print(contingency_table)
          
           No Progression Progression
  Negative             30           0
  Positive              1           3
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at Surveillance p16(+)", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size

#PFS by ctDNA status at surveillance p16(-)

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$p16.status=="Negative",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.Surveillance, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) ~ 
    ctDNA.Surveillance, data = circ_data)

                             n events median 0.95LCL 0.95UCL
ctDNA.Surveillance=NEGATIVE 21      3     NA      NA      NA
ctDNA.Surveillance=POSITIVE 13     11   12.4    10.4      NA
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at Surveillance p16(-)", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")

summary(KM_curve, times= c(0, 12, 24, 36))
Call: survfit(formula = surv_object ~ ctDNA.Surveillance, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.Surveillance=NEGATIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0     21       0    1.000  0.0000        1.000        1.000
   12     18       2    0.905  0.0641        0.670        0.975
   24      9       1    0.840  0.0861        0.576        0.947
   36      3       0    0.840  0.0861        0.576        0.947

                ctDNA.Surveillance=POSITIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0     13       0    1.000   0.000       1.0000        1.000
   12      7       6    0.538   0.138       0.2477        0.760
   24      3       4    0.231   0.117       0.0558        0.475
   36      1       1    0.154   0.100       0.0248        0.388
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data) 
ggforest(cox_fit,data = circ_data) 

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)

  n= 34, number of events= 14 

                             coef exp(coef) se(coef)     z Pr(>|z|)   
ctDNA.SurveillancePOSITIVE 2.1148    8.2877   0.6551 3.228  0.00125 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                           exp(coef) exp(-coef) lower .95 upper .95
ctDNA.SurveillancePOSITIVE     8.288     0.1207     2.295     29.92

Concordance= 0.731  (se = 0.064 )
Likelihood ratio test= 13.44  on 1 df,   p=2e-04
Wald test            = 10.42  on 1 df,   p=0.001
Score (logrank) test = 14.72  on 1 df,   p=1e-04
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 8.29 (2.3-29.92); p = 0.001"
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)

    Pearson's Chi-squared test with Yates' continuity correction

data:  contingency_table
X-squared = 13.622, df = 1, p-value = 0.0002236
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 7.65e-05
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
   3.750528 386.659717
sample estimates:
odds ratio 
  27.99066 
print(contingency_table)
          
           No Progression Progression
  Negative             18           3
  Positive              2          11
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at Surveillance p16(-)", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size

#Multivariate cox regression for PFS ctDNA status at surveillance

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]

circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"), labels = c("Negative", "Positive"))
circ_data$cStage <- factor(circ_data$cStage, levels = c("I/II", "III/IV"))
circ_data$p16.status <- factor(circ_data$p16.status, levels = c("Negative", "Positive"))
circ_data$Prim.Location <- factor(circ_data$Prim.Location, levels = c("Oropharynx", "Larynx/Hypopharynx", "Oral cavity", "Other (paranasal sinus and nasopharyngeal)"))
surv_object <- Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) 
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance + cStage + p16.status + Prim.Location, data=circ_data) 
ggforest(cox_fit, data = circ_data, main = "Multivariate Regression Model for PFS", refLabel = "Reference Group")

test.ph <- cox.zph(cox_fit)

#ctDNA and MTM/mL Dynamics for pts at surveillance window

#Dynamics and MTM/mL plots for patients with ctDNA negative at surveillance
rm(list=ls())
setwd("~/Downloads")
df <- read.csv("CLIA HNSCC ctDNA MTM.csv", stringsAsFactors = FALSE)
df <- df[df$ctDNA.Surveillance=="NEGATIVE",]

df$PFS.Event <- ifelse(df$PFS.Event %in% c("No", "no", "FALSE", "False", "0"), FALSE,
                       ifelse(df$PFS.Event %in% c("Yes", "yes", "TRUE", "True", "1"), TRUE, NA))
df$PFS.Event <- factor(df$PFS.Event, levels = c(FALSE, TRUE))
df <- df %>%
  group_by(PatientName) %>%
  filter(n() >= 2) %>% #keep only pts with at least 2 post-surgery time points
  ungroup()

num_unique <- length(unique(df$PatientName))
cat("Number of unique patients:", num_unique, "\n")
Number of unique patients: 51 
df_patient_pfs <- df %>%
  group_by(PatientName) %>%
  dplyr::summarize(
    PFS_True = any(PFS.Event == TRUE, na.rm = TRUE),
    PFS_False = all(PFS.Event == FALSE, na.rm = TRUE)
  )

num_true <- sum(df_patient_pfs$PFS_True)
num_false <- sum(df_patient_pfs$PFS_False)

cat("Number of unique patients with Event:", num_true, "\n")
Number of unique patients with Event: 3 
cat("Number of unique patients with No Event:", num_false, "\n")
Number of unique patients with No Event: 48 
p <- ggplot(df, aes(x = date.diff.months, 
                    y = MTM.mL, 
                    group = PatientName, 
                    color = PFS.Event)) +
  geom_line() +      # Connect timepoints for each patient
  geom_point() +     # Add points for each timepoint
  # Use a log10 scale for the y-axis with specified breaks
  scale_y_log10(breaks = c(0.01, 0.1, 1, 10, 100),
                labels = c("0.01","0.1", "1", "10", "100")) +
  scale_x_continuous(breaks = seq(0, max(df$date.diff.months, na.rm = TRUE), by = 6)) +
  scale_color_manual(values = c("FALSE" = "blue", "TRUE" = "red")) +
  labs(
    x = "Time Since Surgery or start of definitive treatment (months)",
    y = "Mean Tumor Molecules per mL (MTM/mL)",
    color = "PFS Event"
  ) +
  theme_minimal()
print(p)


#Dynamics and MTM/mL plots for patients with ctDNA positive at surveillance
rm(list=ls())
setwd("~/Downloads")
df <- read.csv("CLIA HNSCC ctDNA MTM.csv", stringsAsFactors = FALSE)
df <- df[df$ctDNA.Surveillance=="POSITIVE",]

df$PFS.Event <- ifelse(df$PFS.Event %in% c("No", "no", "FALSE", "False", "0"), FALSE,
                       ifelse(df$PFS.Event %in% c("Yes", "yes", "TRUE", "True", "1"), TRUE, NA))
df$PFS.Event <- factor(df$PFS.Event, levels = c(FALSE, TRUE))
df <- df %>%
  group_by(PatientName) %>%
  filter(n() >= 2) %>% #keep only pts with at least 2 post-surgery time points
  ungroup()

num_unique <- length(unique(df$PatientName))
cat("Number of unique patients:", num_unique, "\n")
Number of unique patients: 17 
df_patient_pfs <- df %>%
  group_by(PatientName) %>%
  dplyr::summarize(
    PFS_True = any(PFS.Event == TRUE, na.rm = TRUE),
    PFS_False = all(PFS.Event == FALSE, na.rm = TRUE)
  )

num_true <- sum(df_patient_pfs$PFS_True)
num_false <- sum(df_patient_pfs$PFS_False)

cat("Number of unique patients with Event:", num_true, "\n")
Number of unique patients with Event: 14 
cat("Number of unique patients with No Event:", num_false, "\n")
Number of unique patients with No Event: 3 
p <- ggplot(df, aes(x = date.diff.months, 
                    y = MTM.mL, 
                    group = PatientName, 
                    color = PFS.Event)) +
  geom_line() +      # Connect timepoints for each patient
  geom_point() +     # Add points for each timepoint
  # Use a log10 scale for the y-axis with specified breaks
  scale_y_log10(breaks = c(0.01, 0.1, 1, 10, 100),
                labels = c("0.01","0.1", "1", "10", "100")) +
  scale_x_continuous(breaks = seq(0, max(df$date.diff.months, na.rm = TRUE), by = 6)) +
  scale_color_manual(values = c("FALSE" = "blue", "TRUE" = "red")) +
  labs(
    x = "Time Since Surgery or start of definitive treatment (months)",
    y = "Mean Tumor Molecules per mL (MTM/mL)",
    color = "PFS Event"
  ) +
  theme_minimal()
print(p)

#PFS by ctDNA status at surveillance for pts with MRD & Surveillance time points available

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.complete=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.Surveillance, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) ~ 
    ctDNA.Surveillance, data = circ_data)

                             n events median 0.95LCL 0.95UCL
ctDNA.Surveillance=NEGATIVE 42      1     NA      NA      NA
ctDNA.Surveillance=POSITIVE 12      9   13.6    10.4      NA
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at Surveillance", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")

summary(KM_curve, times= c(0, 12, 24, 36))
Call: survfit(formula = surv_object ~ ctDNA.Surveillance, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.Surveillance=NEGATIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0     42       0    1.000  0.0000        1.000        1.000
   12     34       1    0.976  0.0235        0.843        0.997
   24     20       0    0.976  0.0235        0.843        0.997
   36      6       0    0.976  0.0235        0.843        0.997

                ctDNA.Surveillance=POSITIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0     12       0    1.000   0.000       1.0000        1.000
   12      7       5    0.583   0.142       0.2701        0.801
   24      3       4    0.250   0.125       0.0601        0.505
   36      1       0    0.250   0.125       0.0601        0.505
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data) 
ggforest(cox_fit,data = circ_data) 

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)

  n= 54, number of events= 10 

                             coef exp(coef) se(coef)     z Pr(>|z|)    
ctDNA.SurveillancePOSITIVE  3.733    41.789    1.056 3.536 0.000407 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                           exp(coef) exp(-coef) lower .95 upper .95
ctDNA.SurveillancePOSITIVE     41.79    0.02393     5.278     330.9

Concordance= 0.857  (se = 0.057 )
Likelihood ratio test= 24.87  on 1 df,   p=6e-07
Wald test            = 12.5  on 1 df,   p=4e-04
Score (logrank) test = 35.14  on 1 df,   p=3e-09
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 41.79 (5.28-330.88); p = 0"
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
G2;H2;Warningh in stats::chisq.test(x, y, ...) :
  Chi-squared approximation may be incorrectg
print(chi_square_test)

    Pearson's Chi-squared test with Yates' continuity correction

data:  contingency_table
X-squared = 27.984, df = 1, p-value = 1.223e-07
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 3.889e-07
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
    9.555849 5259.013678
sample estimates:
odds ratio 
  98.99253 
print(contingency_table)
          
           No Progression Progression
  Negative             41           1
  Positive              3           9
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at Surveillance", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size

#Time-dependent analysis for PFS in longitudinal time points for pts with MRD & Surveillance time points available

rm(list=ls())
setwd("~/Downloads")
dt <- read_xlsx("CLIA HNSCC Peddada Clinical Data_Time dependent.xlsx") |>
  clean_names() |>
  mutate(across(.cols = c(window_start_date,dfs_date,
                          surveillance_1_date:surveillance_12_date), 
                .fns = ~ as_date(as.Date(.x, format = "%Y-%m-%d"))))
G2;H2;Warningh: Expecting numeric in Z5 / R5C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z8 / R8C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z9 / R9C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z15 / R15C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z17 / R17C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z18 / R18C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z19 / R19C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z22 / R22C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z23 / R23C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z25 / R25C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z26 / R26C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z27 / R27C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z28 / R28C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z29 / R29C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z32 / R32C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z34 / R34C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z35 / R35C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z38 / R38C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z41 / R41C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z47 / R47C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z48 / R48C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z49 / R49C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z56 / R56C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z58 / R58C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z60 / R60C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z63 / R63C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z64 / R64C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z65 / R65C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z67 / R67C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z68 / R68C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z71 / R71C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z72 / R72C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z74 / R74C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z78 / R78C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z81 / R81C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z82 / R82C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z83 / R83C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z86 / R86C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z87 / R87C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z89 / R89C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z90 / R90C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z93 / R93C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z95 / R95C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z96 / R96C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z98 / R98C26: got a dateg
G2;H2;Warningh: Expecting numeric in Z99 / R99C26: got a dateg
dt_biomarker <- dt |>
  select(pts_id, ct_dna_complete,
         window_start_date,
         surveillance_1_status:surveillance_12_date) |>
  filter(ct_dna_complete) |>
  pivot_longer(cols = surveillance_1_status:surveillance_12_date,
               names_to = c("visit_number", ".value"),
               names_pattern = "surveillance_(.)_(.*)") |>
  mutate(biomarker_time = day(days(date - window_start_date))) |>
  select(pts_id, biomarker_time, biomarker_status = status) |>
  filter(!is.na(biomarker_time))

glimpse(dt_biomarker)
Rows: 183
Columns: 3
$ pts_id           <chr> "UNM-004", "UNM-004", "UNM-004", "UNM-008", "UNM-008", "UNM-008", "UNM-008", "UNM-008", "UNM-009", "UNM-009", "UNM-009", "UNM-009", "UNM-009", "UNM-014", "UNM-016"…
$ biomarker_time   <dbl> 18, 25719, 179, -75, 25647, 154, 236, 322, 46, 25792, 327, 418, 507, 156, 112, 25865, 387, 481, 19, 25756, 273, 361, 454, 550, 649, 32, 25783, 307, 398, 502, 579, …
$ biomarker_status <chr> "NEGATIVE", "POSITIVE", "POSITIVE", "NEGATIVE", "NEGATIVE", "NEGATIVE", "NEGATIVE", NA, "NEGATIVE", "NEGATIVE", "NEGATIVE", "NEGATIVE", "NEGATIVE", "NEGATIVE", "NE…
dt_survival <- dt |>
  select(pts_id, ct_dna_complete,
         window_start_date:dfs_date, dfs_event) |>  # Added dfs_event here
  filter(ct_dna_complete) |>
  mutate(dfs_time = (dfs_date - window_start_date),
         dfs_time = day(days(dfs_time)),
         dfs_event = as.numeric(dfs_event)) |>
  select(pts_id, dfs_time, dfs_event)

glimpse(dt_survival)
Rows: 54
Columns: 3
$ pts_id    <chr> "UNM-004", "UNM-008", "UNM-009", "UNM-014", "UNM-016", "UNM-019", "UNM-020", "UNM-023", "UNM-024", "UNM-025", "UNM-026", "UNM-027", "UNM-029", "UNM-030", "UNM-031", "UNM-…
$ dfs_time  <dbl> 308, 953, 513, 208, 655, 1065, 897, 237, 535, 880, 638, 934, 989, 437, 113, 647, 535, 943, 1069, 591, 305, 283, 126, 918, 192, 411, 1027, 156, 1048, 270, 565, 338, 141, 8…
$ dfs_event <dbl> 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
aux <- dt_survival %>% 
  filter(dfs_time <= 0)

tab <- left_join(aux, dt) |>
  select(pts_id, window_start_date, dfs_time, dfs_date,
         surveillance_1_date:surveillance_12_date) |>
  mutate(across(.cols = dfs_date:surveillance_12_date, 
                .fns = ~ as_date(.x))) |>
  select(pts_id, window_start_date, dfs_date, dfs_time)
Joining with `by = join_by(pts_id, dfs_event)`
datatable(tab, filter = "top")

dt_survival <- dt_survival |>
  filter(dfs_time > 0)

aux <- dt |>
  select(pts_id, ct_dna_complete,
         window_start_date, dfs_date,
         surveillance_1_date:surveillance_12_date) |>
  mutate(across(.cols = surveillance_1_date:surveillance_12_date, 
                .fns = ~ .x - window_start_date)) |>
  mutate(across(.cols = surveillance_1_date:surveillance_12_date, 
                .fns = ~ .x < 0)) |>
  rowwise() |>
  mutate(sum_neg = 
           sum(c_across(surveillance_1_date:surveillance_12_date),
               na.rm = TRUE))  |>
  select(pts_id, sum_neg)

tab <- left_join(aux, dt) |>
  filter(sum_neg > 0) |>
  select(pts_id, sum_neg, window_start_date,
         surveillance_1_date:surveillance_12_date) |>
  mutate(across(.cols = window_start_date:surveillance_12_date, 
                .fns = ~ as_date(.x))) 
Joining with `by = join_by(pts_id)`
G2;H2;Warningh in left_join(aux, dt) :
  Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 99 of `x` matches multiple rows in `y`.
ℹ Row 99 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship = "many-to-many"` to silence this warning.g
datatable(tab, filter = "top")

aux <- dt |>
  select(pts_id, ct_dna_complete,
         window_start_date, dfs_date,
         surveillance_1_date:surveillance_12_date) |>
  mutate(across(.cols = dfs_date:surveillance_12_date, 
                .fns = ~ .x - window_start_date)) |>
  mutate(across(.cols = surveillance_2_date:surveillance_12_date,
                .fns = ~ dfs_date < .x)) |>
  rowwise() |>
  mutate(n_biomarker_after_event = sum(c_across(surveillance_2_date:
                                                  surveillance_12_date), 
                                       na.rm = TRUE)) |>
  mutate(across(.cols = surveillance_1_date:surveillance_12_date,
                .fns = ~ !is.na(.x))) |>
  mutate(total_biomarker = sum(c_across(surveillance_2_date:
                                          surveillance_12_date), 
                               na.rm = TRUE)) |>
  select(pts_id, n_biomarker_after_event, total_biomarker)

temp <- aux |> 
  select(-pts_id) |> 
  group_by(n_biomarker_after_event, total_biomarker) |>  # Direct grouping
  summarise(freq = n(), .groups = "drop")  # Drop groups after summarization


tab <- left_join(aux, dt) |>
  select(pts_id, n_biomarker_after_event, total_biomarker, 
         dfs_date,
         surveillance_2_date:surveillance_12_date) |>
  mutate(across(.cols = dfs_date:surveillance_12_date, 
                .fns = ~ as_date(.x))) |>
  filter(n_biomarker_after_event > 0)
Joining with `by = join_by(pts_id)`
G2;H2;Warningh in left_join(aux, dt) :
  Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 99 of `x` matches multiple rows in `y`.
ℹ Row 99 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship = "many-to-many"` to silence this warning.g
datatable(tab, filter = "top")

aux <- tmerge(data1 = dt_survival, 
              data2 = dt_survival,
              id = pts_id, 
              dfs_event = event(dfs_time, dfs_event))
dt_final <- tmerge(data1 = aux, 
                   data2 = dt_biomarker,
                   id = pts_id, 
                   biomarker_status = 
                     tdc(biomarker_time, biomarker_status))

datatable(dt_final, filter = "top")

# Syntax if there is not time-dependent covariate
# fit <- coxph(Surv(dfs_time, dfs_event) ~ biomarker_status,
#              data = dt_final)
# summary(fit)

fit <- coxph(Surv(tstart, tstop, dfs_event) ~ biomarker_status,
             data = dt_final)
summary(fit)
Call:
coxph(formula = Surv(tstart, tstop, dfs_event) ~ biomarker_status, 
    data = dt_final)

  n= 141, number of events= 10 
   (52 observations deleted due to missingness)

                            coef exp(coef) se(coef)     z Pr(>|z|)    
biomarker_statusPOSITIVE  3.2868   26.7577   0.6568 5.004 5.62e-07 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                         exp(coef) exp(-coef) lower .95 upper .95
biomarker_statusPOSITIVE     26.76    0.03737     7.385     96.95

Concordance= 0.756  (se = 0.079 )
Likelihood ratio test= 21.73  on 1 df,   p=3e-06
Wald test            = 25.04  on 1 df,   p=6e-07
Score (logrank) test = 54.47  on 1 df,   p=2e-13
cox_fit_summary <- summary(fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 26.76 (7.38-96.95); p = 0"

#Median numbers of time points and lead time in the longitudinal setting for pts with MRD & Surveillance time points available

# Load the dataset
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.complete=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)

median_Nsurvtps <- median(circ_datadf$Nsurvtps, na.rm = TRUE)
min_Nsurvtps <- min(circ_datadf$Nsurvtps, na.rm = TRUE)
max_Nsurvtps <- max(circ_datadf$Nsurvtps, na.rm = TRUE)

cat(sprintf("Median # of surveillance time points: %d (%d-%d)\n", 
            median_Nsurvtps, min_Nsurvtps, max_Nsurvtps))
Median # of surveillance time points: 4 (1-13)
circ_datadf$LeadTime_Months <- circ_datadf$LeadTime / 30.437
median_LeadTime <- median(circ_datadf$LeadTime_Months, na.rm = TRUE)
min_LeadTime <- min(circ_datadf$LeadTime_Months, na.rm = TRUE)
max_LeadTime <- max(circ_datadf$LeadTime_Months, na.rm = TRUE)
cat(sprintf("Longitudinally, ctDNA positivity preceded progression by a median of %.2f mo (%.2f–%.2f)\n", 
            median_LeadTime, min_LeadTime, max_LeadTime))
Longitudinally, ctDNA positivity preceded progression by a median of 4.07 mo (0.62–13.96)

#PFS by ctDNA status anytime

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.anytime!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.anytime, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) ~ 
    ctDNA.anytime, data = circ_data)

                        n events median 0.95LCL 0.95UCL
ctDNA.anytime=NEGATIVE 55      3     NA      NA      NA
ctDNA.anytime=POSITIVE 30     22   13.2    10.4    24.4
event_summary <- circ_data %>%
  group_by(ctDNA.anytime) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.anytime, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA anytime", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")

summary(KM_curve, times= c(0, 12, 24, 36))
Call: survfit(formula = surv_object ~ ctDNA.anytime, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.anytime=NEGATIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0     55       0    1.000  0.0000        1.000        1.000
   12     44       2    0.964  0.0252        0.862        0.991
   24     26       1    0.935  0.0371        0.807        0.979
   36      9       0    0.935  0.0371        0.807        0.979

                ctDNA.anytime=POSITIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0     30       0    1.000  0.0000       1.0000        1.000
   12     16      13    0.567  0.0905       0.3733        0.721
   24      7       7    0.305  0.0876       0.1485        0.478
   36      3       2    0.218  0.0814       0.0851        0.390
circ_data$ctDNA.anytime <- factor(circ_data$ctDNA.anytime, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.anytime, data=circ_data) 
ggforest(cox_fit,data = circ_data) 

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.anytime, data = circ_data)

  n= 85, number of events= 25 

                         coef exp(coef) se(coef)     z Pr(>|z|)    
ctDNA.anytimePOSITIVE  2.9697   19.4860   0.6169 4.814 1.48e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                      exp(coef) exp(-coef) lower .95 upper .95
ctDNA.anytimePOSITIVE     19.49    0.05132     5.815     65.29

Concordance= 0.799  (se = 0.037 )
Likelihood ratio test= 40.22  on 1 df,   p=2e-10
Wald test            = 23.17  on 1 df,   p=1e-06
Score (logrank) test = 45.33  on 1 df,   p=2e-11
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 19.49 (5.82-65.29); p = 0"
circ_data$ctDNA.anytime <- factor(circ_data$ctDNA.anytime, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.anytime, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)

    Pearson's Chi-squared test with Yates' continuity correction

data:  contingency_table
X-squared = 39.873, df = 1, p-value = 2.71e-10
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 7.175e-11
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
  10.20553 282.29143
sample estimates:
odds ratio 
  44.11145 
print(contingency_table)
          
           No Progression Progression
  Negative             52           3
  Positive              8          22
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status anytime", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size

#OS by ctDNA status anytime

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.anytime!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.anytime, data = circ_data)
Call: survfit(formula = Surv(time = circ_data$OS.months, event = circ_data$OS.Event) ~ 
    ctDNA.anytime, data = circ_data)

                        n events median 0.95LCL 0.95UCL
ctDNA.anytime=NEGATIVE 55      1     NA      NA      NA
ctDNA.anytime=POSITIVE 30      7     NA      NA      NA
event_summary <- circ_data %>%
  group_by(ctDNA.anytime) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.anytime, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA anytime", ylab= "Overall Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")

summary(KM_curve, times= c(12, 24, 36))
Call: survfit(formula = surv_object ~ ctDNA.anytime, data = circ_data, 
    conf.int = 0.95, conf.type = "log-log")

                ctDNA.anytime=NEGATIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     45       1    0.982   0.018        0.878        0.997
   24     26       0    0.982   0.018        0.878        0.997
   36      9       0    0.982   0.018        0.878        0.997

                ctDNA.anytime=POSITIVE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     24       4    0.867  0.0621        0.683        0.948
   24     13       3    0.735  0.0885        0.516        0.867
   36      8       0    0.735  0.0885        0.516        0.867
circ_data$ctDNA.anytime <- factor(circ_data$ctDNA.anytime, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.anytime, data=circ_data) 
ggforest(cox_fit,data = circ_data) 

summary(cox_fit)
Call:
coxph(formula = surv_object ~ ctDNA.anytime, data = circ_data)

  n= 85, number of events= 8 

                        coef exp(coef) se(coef)     z Pr(>|z|)  
ctDNA.anytimePOSITIVE  2.621    13.743    1.069 2.451   0.0142 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                      exp(coef) exp(-coef) lower .95 upper .95
ctDNA.anytimePOSITIVE     13.74    0.07276     1.691     111.7

Concordance= 0.766  (se = 0.066 )
Likelihood ratio test= 10  on 1 df,   p=0.002
Wald test            = 6.01  on 1 df,   p=0.01
Score (logrank) test = 10.33  on 1 df,   p=0.001
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
[1] "HR = 13.74 (1.69-111.72); p = 0.014"
circ_data$ctDNA.anytime <- factor(circ_data$ctDNA.anytime, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.anytime, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)
G2;H2;Warningh in stats::chisq.test(x, y, ...) :
  Chi-squared approximation may be incorrectg
print(chi_square_test)

    Pearson's Chi-squared test with Yates' continuity correction

data:  contingency_table
X-squared = 8.1668, df = 1, p-value = 0.004266
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 0.002448
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
   1.874902 750.814710
sample estimates:
odds ratio 
  15.89819 
print(contingency_table)
          
           Alive Deceased
  Negative    54        1
  Positive    23        7
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status anytime", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Living Status",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size

#Median numbers of time points and lead time anytime post-surery or definitive treatment

# Load the dataset
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.anytime!="",]
circ_datadf <- as.data.frame(circ_data)

median_Nsurvtps <- median(circ_datadf$Ntotaltps, na.rm = TRUE)
min_Nsurvtps <- min(circ_datadf$Ntotaltps, na.rm = TRUE)
max_Nsurvtps <- max(circ_datadf$Ntotaltps, na.rm = TRUE)

cat(sprintf("Median # of time points anytimes: %d (%d-%d)\n", 
            median_Nsurvtps, min_Nsurvtps, max_Nsurvtps))
Median # of time points anytimes: 4 (1-16)
circ_datadf$LeadTime_Months <- circ_datadf$Anytime.LeadTime / 30.437
median_LeadTime <- median(circ_datadf$LeadTime_Months, na.rm = TRUE)
min_LeadTime <- min(circ_datadf$LeadTime_Months, na.rm = TRUE)
max_LeadTime <- max(circ_datadf$LeadTime_Months, na.rm = TRUE)
cat(sprintf("Anytime post-surgery or start of definitive treatment, ctDNA positivity preceded progression by a median of %.2f mo (%.2f–%.2f)\n", 
            median_LeadTime, min_LeadTime, max_LeadTime))
Anytime post-surgery or start of definitive treatment, ctDNA positivity preceded progression by a median of 3.12 mo (0.00–21.49)
---
title: "CLIA HNSCC UNM Clinical Analysis 07272025"
output: html_notebook
---

library(swimplot)
library(grid)
library(gtable)
library(readr) 
library(mosaic)
library(dplyr) 
library(survival) 
library(survminer) 
library(ggplot2)
library(scales)
library(coxphf)
library(ggthemes)
library(tidyverse)
library(gtsummary)
library(flextable)
library(reshape2)
library(parameters)
library(car)
library(ComplexHeatmap)
library(tidyverse)
library(readxl)
library(janitor)
library(DT)
library(pROC)
library(rms)

#ctDNA Detection Rates by Window and Stages
```{r}
#ctDNA at Baseline
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_data$ctDNA.Base <- factor(circ_data$ctDNA.Base, levels=c("NEGATIVE","POSITIVE"))
circ_data <- subset(circ_data, ctDNA.Base %in% c("NEGATIVE", "POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I/II","III/IVA/IVB","IVC"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.Base == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.Base, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.Base == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#ctDNA at MRD
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I/II","III/IVA/IVB","IVC"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.MRD == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.MRD, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.MRD == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#ctDNA at Surveillance
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I/II","III/IVA/IVB","IVC"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.Surveillance == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.Surveillance, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.Surveillance == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)
```



#Demographics Table
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]

circ_data_subset <- circ_data %>%
  select(
    Sex,
    Age,
    Tobacco.History,
    Prim.Location,
    cT,
    cN,
    cM,
    Histology,
    Stage,
    p16.status,
    Treatment.Group,
    PFS.Event,
    OS.Event,
    OS.months) %>%
  mutate(
    Sex = factor(Sex),
    Age = as.numeric(Age),
    Tobacco.History = factor(Tobacco.History),
    Prim.Location = factor(Prim.Location),
    cT = factor(cT),
    cN = factor(cN),
    cM = factor(cM),
    Histology = factor(Histology),
    Stage = factor(Stage),
    p16.status = factor(p16.status),
    Treatment.Group = factor(Treatment.Group),
    PFS.Event = factor(PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression")),
    OS.Event = factor(OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased")),
    OS.months = as.numeric(OS.months)) 
table1 <- circ_data_subset %>%
  tbl_summary(
    statistic = list(
      all_continuous() ~ "{median} ({min} - {max})",
      all_categorical() ~ "{n} ({p}%)")) %>%
  bold_labels()
table1
fit1 <- as_flex_table(
  table1,
  include = everything(),
  return_calls = FALSE
)
fit1
save_as_docx(fit1, path= "~/Downloads/1. CLIA HNSCC UNM Demographics Table.docx")
```



#Demographics Table by ctDNA at baseline
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]

circ_data_subset1 <- circ_data %>%
  select(
    Sex,
    Age,
    Tobacco.History,
    Prim.Location,
    cT,
    cN,
    cM,
    Histology,
    Stage,
    p16.status,
    Treatment.Group,
    PFS.Event,
    OS.Event,
    OS.months) %>%
  mutate(
    Sex = factor(Sex),
    Age = as.numeric(Age),
    Tobacco.History = factor(Tobacco.History),
    Prim.Location = factor(Prim.Location),
    cT = factor(cT),
    cN = factor(cN),
    cM = factor(cM),
    Histology = factor(Histology),
    Stage = factor(Stage),
    p16.status = factor(p16.status),
    Treatment.Group = factor(Treatment.Group),
    PFS.Event = factor(PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression")),
    OS.Event = factor(OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased")),
    OS.months = as.numeric(OS.months)) 

circ_data1 <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]

circ_data_subset2 <- circ_data1 %>%
  select(
    Sex,
    Age,
    Tobacco.History,
    Prim.Location,
    cT,
    cN,
    cM,
    Histology,
    Stage,
    p16.status,
    Treatment.Group,
    PFS.Event,
    OS.Event,
    OS.months,
    ctDNA.Base) %>%
  mutate(
    Sex = factor(Sex),
    Age = as.numeric(Age),
    Tobacco.History = factor(Tobacco.History),
    Prim.Location = factor(Prim.Location),
    cT = factor(cT),
    cN = factor(cN),
    cM = factor(cM),
    Histology = factor(Histology),
    Stage = factor(Stage),
    p16.status = factor(p16.status),
    Treatment.Group = factor(Treatment.Group),
    PFS.Event = factor(PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression")),
    OS.Event = factor(OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased")),
    OS.months = as.numeric(OS.months),
    ctDNA.Base = factor(ctDNA.Base, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive")))
Overall <- circ_data_subset1 %>%
  tbl_summary(
    statistic = list(
      all_continuous() ~ "{median} ({min} - {max})",
      all_categorical() ~ "{n} ({p}%)")) %>%
  bold_labels()
Overall

ByctDNA_MRD <- circ_data_subset2 %>%
  tbl_summary(
    by = ctDNA.Base, # add this line to subgroup by ctDNA.Base
    statistic = list(
      all_continuous() ~ "{median} ({min} - {max})",
      all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels()
ByctDNA_MRD

merged_table <- tbl_merge(tbls=list(Overall, ByctDNA_MRD))
merged_table

fit1 <- as_flex_table(
  merged_table,
  include = everything(),
  return_calls = FALSE
)
fit1
save_as_docx(fit1, path = "~/Downloads/1b. CLIA HNSCC UNM Demographics Table by ctDNA.docx")
```

#Overview plot by Stage
```{r}
setwd("~/Downloads") 
clinstage <- read.csv("CLIA HNSCC UNM_OP.csv")
clinstage_df <- as.data.frame(clinstage)

# Creating the basic swimmer plot
oplot <- swimmer_plot(df=clinstage_df,
                      id='PatientName',
                      end='fu.diff.months',
                      fill='gray',
                      width=.01,
                      base_size = 14,
                      stratify= c('Stage'))

# Adding themes and scales
oplot <- oplot + theme(panel.border = element_blank())
oplot <- oplot + scale_y_continuous(breaks = seq(0, 72, by = 3))
oplot <- oplot + labs(x ="Patients", y="Months from Diagnosis")

# Adding swimmer points
oplot_ev1 <- oplot + swimmer_points(df_points=clinstage_df,
                                    id='PatientName',
                                    time='date.diff.months',
                                    name_shape ='Event_type',
                                    name_col = 'Event',
                                    size=3.5,fill='black')
# Optionally uncomment and use col='darkgreen' if needed

# Adding shape manual scale
oplot_ev1.1 <- oplot_ev1 + ggplot2::scale_shape_manual(name="Event_type",
                                                       values=c(1,16,6,18,18,4),
                                                       breaks=c('ctDNA_neg','ctDNA_pos', 'Imaging','Surgery','Biopsy', 'Death'))

# Display the plot
oplot_ev1.1
oplot_ev2 <- oplot_ev1.1 + swimmer_lines(df_lines=clinstage_df,
                                         id='PatientName',
                                         start='Tx_start.months',
                                         end='Tx_end.months',
                                         name_col='Tx_type',
                                         size=3.5,
                                         name_alpha = 1.0)
oplot_ev2 <- oplot_ev2 + guides(linetype = guide_legend(override.aes = list(size = 5, color = "black")))
oplot_ev2
oplot_ev2.2 <- oplot_ev2 + ggplot2::scale_color_manual(name="Event",values=c( "grey", "orange", "black", "black", "green", "red", "purple", "blue"))
oplot_ev2.2
```

#PFS in Complete Cohort (N=97)
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.available, data = circ_data)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.available, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue"), title="PFS - Complete Cohort (n=97)", ylab= "Progression-Free Survival", xlab="Months from Start of definitive Treatment", legend.labs=c("Complete cohort"), legend.title="")
summary(KM_curve, times= c(12, 24, 36))
```

#OS in Complete Cohort (N=97)
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.available, data = circ_data)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.available, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue"), title="OS - Complete Cohort (n=97)", ylab= "Overall Survival", xlab="Months from Start of definitive Treatment", legend.labs=c("Complete cohort"), legend.title="")
summary(KM_curve, times= c(12, 24, 36))
```

#Association of Baseline ctDNA MTM levels with clinicopathological factors
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

tally(~cStage, data=circ_data, margins = TRUE)
circ_data$cStage <- factor(circ_data$cStage, levels = c("I/II","III/IV"), labels = c("I/II (n=34)","III/IV (n=29)"))
boxplot(ctDNA.Base.MTM~cStage, data=circ_data, main="ctDNA pre-treatment MTM - Stage", xlab="Stage", ylab="MTM/mL", col="white",border="black", ylim = c(0, 200))
median_ctDNA.Stage <- circ_data %>%
  group_by(cStage) %>%
  summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.Stage)
m1<-wilcox.test(ctDNA.Base.MTM ~ cStage, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m1)

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

tally(~cT.status, data=circ_data, margins = TRUE)
circ_data$cT.status <- factor(circ_data$cT.status, levels = c("T0-T2","T3-T4"), labels = c("T0-T2 (n=28)","T3-T4 (n=34)"))
boxplot(ctDNA.Base.MTM~cT.status, data=circ_data, main="ctDNA pre-treatment MTM - T stage", xlab="T stage", ylab="MTM/mL", col="white",border="black", ylim = c(0, 200))
median_ctDNA.cT <- circ_data %>%
  group_by(cT.status) %>%
  summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.cT)
m2<-wilcox.test(ctDNA.Base.MTM ~ cT.status, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m2)

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

tally(~cT, data=circ_data, margins = TRUE)
circ_data$cT <- factor(circ_data$cT, levels = c("T0","T1","T2","T3","T4"))
boxplot(ctDNA.Base.MTM~cT, data=circ_data, main="ctDNA pre-treatment MTM - cT status", xlab="cT status", ylab="MTM/mL", col="white",border="black", ylim = c(0, 200))
median_ctDNA.cT <- circ_data %>%
  group_by(cT) %>%
  summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.cT)
pairwise_wilcox <- pairwise.wilcox.test(circ_data$ctDNA.Base.MTM, circ_data$cT, 
                                        p.adjust.method = "none", 
                                        exact = FALSE)
print(pairwise_wilcox)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available == "TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base != "",]
circ_data$cT <- factor(circ_data$cT, levels = c("T0", "T1", "T2", "T3", "T4"))
circ_data$ctDNA.Base.MTM <- as.numeric(circ_data$ctDNA.Base.MTM)
cT_levels <- levels(circ_data$cT)
p_value_matrix <- matrix(NA, nrow = length(cT_levels), ncol = length(cT_levels))
rownames(p_value_matrix) <- cT_levels
colnames(p_value_matrix) <- cT_levels

for (i in 1:length(cT_levels)) {
  for (j in i:length(cT_levels)) {
    if (i != j) {
      # Extract data for both groups
      data1 <- circ_data %>% filter(cT == cT_levels[i]) %>% pull(ctDNA.Base.MTM)
      data2 <- circ_data %>% filter(cT == cT_levels[j]) %>% pull(ctDNA.Base.MTM)
      
      # Perform Wilcoxon test and store p-value
      test_result <- wilcox.test(data1, data2, exact = FALSE)
      p_value_matrix[i, j] <- test_result$p.value
      p_value_matrix[j, i] <- test_result$p.value  # Make symmetric
    } else {
      p_value_matrix[i, j] <- 1  # Self-comparison = 1
    }
  }
}

p_value_matrix[is.na(p_value_matrix)] <- 1.00
p_value_data <- melt(p_value_matrix)
colnames(p_value_data) <- c("cT1", "cT2", "p_value")
p_value_data <- p_value_data %>%
  mutate(
    significance = case_when(
      p_value < 0.001 ~ "***",
      p_value < 0.01 ~ "**",
      p_value < 0.05 ~ "*",
      TRUE ~ ""
    )
  )

ggplot(p_value_data, aes(x = cT1, y = cT2, fill = p_value)) +
  geom_tile(color = "white", size = 0.8) +  # Thicker grid lines for separation
  geom_text(aes(label = significance), color = "black", size = 6, fontface = "bold") +  # Significance markers
  scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0.05) +  # Gradient colors
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 12, face = "bold"),
        axis.text.y = element_text(size = 12, face = "bold"),
        panel.grid = element_blank()) +
  labs(title = "Pairwise Wilcoxon-Test P-Values (ctDNA.Base.MTM by cT)",
       x = "cT Status", y = "cT Status", fill = "P-Value")

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

tally(~cN.status, data=circ_data, margins = TRUE)
circ_data$cN.status <- factor(circ_data$cN.status, levels = c("N0","N1-N3"), labels = c("N0 (n=12)","N1-N3 (n=50)"))
boxplot(ctDNA.Base.MTM~cN.status, data=circ_data, main="ctDNA pre-treatment MTM - cN status", xlab="cN status", ylab="MTM/mL", col="white",border="black", ylim = c(0, 200))
median_ctDNA.cN <- circ_data %>%
  group_by(cN.status) %>%
  summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.cN)
m3<-wilcox.test(ctDNA.Base.MTM ~ cN.status, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m3)

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

tally(~cN, data=circ_data, margins = TRUE)
circ_data$cN <- factor(circ_data$cN, levels = c("N0","N1","N2","N3"))
boxplot(ctDNA.Base.MTM~cN, data=circ_data, main="ctDNA pre-treatment MTM - N Stage", xlab="N Stage", ylab="MTM/mL", col="white",border="black", ylim = c(0, 500))
median_ctDNA.cN <- circ_data %>%
  group_by(cN) %>%
  summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.cN)
pairwise_wilcox <- pairwise.wilcox.test(circ_data$ctDNA.Base.MTM, circ_data$cN, 
                                        p.adjust.method = "none", 
                                        exact = FALSE)
print(pairwise_wilcox)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available == "TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base != "",]
circ_data$cN <- factor(circ_data$cN, levels = c("N0","N1","N2","N3"))
circ_data$ctDNA.Base.MTM <- as.numeric(circ_data$ctDNA.Base.MTM)
cN_levels <- levels(circ_data$cN)
p_value_matrix <- matrix(NA, nrow = length(cN_levels), ncol = length(cN_levels))
rownames(p_value_matrix) <- cN_levels
colnames(p_value_matrix) <- cN_levels

for (i in 1:length(cN_levels)) {
  for (j in i:length(cN_levels)) {
    if (i != j) {
      # Extract data for both groups
      data1 <- circ_data %>% filter(cN == cN_levels[i]) %>% pull(ctDNA.Base.MTM)
      data2 <- circ_data %>% filter(cN == cN_levels[j]) %>% pull(ctDNA.Base.MTM)
      
      # Perform Wilcoxon test and store p-value
      test_result <- wilcox.test(data1, data2, exact = FALSE)
      p_value_matrix[i, j] <- test_result$p.value
      p_value_matrix[j, i] <- test_result$p.value  # Make symmetric
    } else {
      p_value_matrix[i, j] <- 1  # Self-comparison = 1
    }
  }
}

p_value_matrix[is.na(p_value_matrix)] <- 1.00
p_value_data <- melt(p_value_matrix)
colnames(p_value_data) <- c("cN1", "cN2", "p_value")
p_value_data <- p_value_data %>%
  mutate(
    significance = case_when(
      p_value < 0.001 ~ "***",
      p_value < 0.01 ~ "**",
      p_value < 0.05 ~ "*",
      TRUE ~ ""
    )
  )

ggplot(p_value_data, aes(x = cN1, y = cN2, fill = p_value)) +
  geom_tile(color = "white", size = 0.8) +  # Thicker grid lines for separation
  geom_text(aes(label = significance), color = "black", size = 6, fontface = "bold") +  # Significance markers
  scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0.05) +  # Gradient colors
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 12, face = "bold"),
        axis.text.y = element_text(size = 12, face = "bold"),
        panel.grid = element_blank()) +
  labs(title = "Pairwise Wilcoxon-Test P-Values (ctDNA.Base.MTM by cN)",
       x = "Status", y = "cN Status", fill = "P-Value")

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available == "TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base != "",]
circ_data$cT <- factor(circ_data$cT, levels = c("T0", "T1", "T2", "T3", "T4"))
circ_data$cN <- factor(circ_data$cN, levels = c("N0", "N1", "N2", "N3"))
circ_data$ctDNA.Base.MTM <- as.numeric(circ_data$ctDNA.Base.MTM)

median_ctDNA <- circ_data %>%
  group_by(cT, cN) %>%
  summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE)) %>%
  ungroup()

p_value_matrix <- dcast(median_ctDNA, cT ~ cN, value.var = "median_ctDNA_Base_MTM")
p_value_data <- melt(p_value_matrix, id.vars = "cT", variable.name = "cN", value.name = "median_value")
p_value_data$missing <- ifelse(is.na(p_value_data$median_value), "Missing", "Present")
p_value_data$median_value[is.na(p_value_data$median_value)] <- 0

ggplot(p_value_data, aes(x = cN, y = cT, fill = median_value)) +
  geom_tile(color = "black", size = 0.5) +  # Black gridlines for separation
  geom_text(aes(label = round(median_value, 2)), color = "black", size = 5) +  # Display median values
  scale_fill_gradient(low = "white", high = "blue") +  # Color gradient similar to the reference image
  theme_minimal() +
  theme(axis.text.x = element_text(size = 12, face = "bold"),
        axis.text.y = element_text(size = 12, face = "bold"),
        panel.grid = element_blank()) +
  labs(title = "Median ctDNA.Base.MTM by cT and cN",
       x = "cN Status", y = "cT Status", fill = "Median MTM") +
  geom_tile(data = subset(p_value_data, missing == "Missing"), 
            aes(x = cN, y = cT), color = "black", fill = NA, size = 0.5, linetype = "dashed")  # Add diagonal cross for missing values

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

tally(~cM, data=circ_data, margins = TRUE)
circ_data$cM <- factor(circ_data$cM, levels = c("M0","M1"), labels = c("M0 (n=60)","M1 (n=2)"))
boxplot(ctDNA.Base.MTM~cM, data=circ_data, main="ctDNA pre-treatment MTM - cM", xlab="cM", ylab="MTM/mL", col="white",border="black", ylim = c(0, 500))
median_ctDNA.cM <- circ_data %>%
  group_by(cM) %>%
  summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.cM)
m4<-wilcox.test(ctDNA.Base.MTM ~ cM, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m4)

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

tally(~p16.status, data=circ_data, margins = TRUE)
circ_data$p16.status <- factor(circ_data$p16.status, levels = c("Negative","Positive"), labels = c("p16 neg (n=23)","p16 pos (n=39)"))
boxplot(ctDNA.Base.MTM~p16.status, data=circ_data, main="ctDNA pre-treatment MTM - p16 status", xlab="p16 status", ylab="MTM/mL", col="white",border="black", ylim = c(0, 200))
median_ctDNA.p16 <- circ_data %>%
  group_by(p16.status) %>%
  summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.p16)
m5<-wilcox.test(ctDNA.Base.MTM ~ p16.status, data=circ_data, na.rm=TRUE, exact=FALSE, conf.int=TRUE)
print(m5)

rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base!="",]
circ_datadf <- as.data.frame(circ_data)

tally(~Prim.Location, data=circ_data, margins = TRUE)
circ_data$Prim.Location <- factor(circ_data$Prim.Location, levels = c("Larynx/Hypopharynx","Oral cavity", "Oropharynx", "Other (paranasal sinus and nasopharyngeal)"))
boxplot(ctDNA.Base.MTM~Prim.Location, data=circ_data, main="ctDNA pre-treatment MTM - Tumor Location", xlab="Tumor Location", ylab="MTM/mL", col="white",border="black", ylim = c(0, 200))
median_ctDNA.loc <- circ_data %>%
  group_by(Prim.Location) %>%
  summarise(median_ctDNA_Base_MTM = median(ctDNA.Base.MTM, na.rm = TRUE))
print(median_ctDNA.loc)
pairwise_wilcox <- pairwise.wilcox.test(circ_data$ctDNA.Base.MTM, circ_data$Prim.Location, 
                                        p.adjust.method = "none", 
                                        exact = FALSE)

print(pairwise_wilcox)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available == "TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Base != "",]
circ_data$Prim.Location <- factor(circ_data$Prim.Location, levels = c("Larynx/Hypopharynx","Oral cavity", "Oropharynx", "Other (paranasal sinus and nasopharyngeal)"), labels = c("LRX/HPRX","OC","PRX","Other"))
circ_data$ctDNA.Base.MTM <- as.numeric(circ_data$ctDNA.Base.MTM)
pl_levels <- levels(circ_data$Prim.Location)
p_value_matrix <- matrix(NA, nrow = length(pl_levels), ncol = length(pl_levels))
rownames(p_value_matrix) <- pl_levels
colnames(p_value_matrix) <- pl_levels

for (i in 1:length(pl_levels)) {
  for (j in i:length(pl_levels)) {
    if (i != j) {
      # Extract data for both groups
      data1 <- circ_data %>% filter(Prim.Location == pl_levels[i]) %>% pull(ctDNA.Base.MTM)
      data2 <- circ_data %>% filter(Prim.Location == pl_levels[j]) %>% pull(ctDNA.Base.MTM)
      
      # Perform Wilcoxon test and store p-value
      test_result <- wilcox.test(data1, data2, exact = FALSE)
      p_value_matrix[i, j] <- test_result$p.value
      p_value_matrix[j, i] <- test_result$p.value  # Make symmetric
    } else {
      p_value_matrix[i, j] <- 1  # Self-comparison = 1
    }
  }
}

p_value_matrix[is.na(p_value_matrix)] <- 1.00
p_value_data <- melt(p_value_matrix)
colnames(p_value_data) <- c("pl1", "pl2", "p_value")
p_value_data <- p_value_data %>%
  mutate(
    significance = case_when(
      p_value < 0.001 ~ "***",
      p_value < 0.01 ~ "**",
      p_value < 0.05 ~ "*",
      TRUE ~ ""
    )
  )

ggplot(p_value_data, aes(x = pl1, y = pl2, fill = p_value)) +
  geom_tile(color = "white", size = 0.8) +  # Thicker grid lines for separation
  geom_text(aes(label = significance), color = "black", size = 6, fontface = "bold") +  # Significance markers
  scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0.05) +  # Gradient colors
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 12, face = "bold"),
        axis.text.y = element_text(size = 12, face = "bold"),
        panel.grid = element_blank()) +
  labs(title = "Pairwise Wilcoxon-Test P-Values (ctDNA.Base.MTM by Tumor Location)",
       x = "Tumor Location", y = "Tumor Location", fill = "P-Value")
```

#PFS by ctDNA status at MRD
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at MRD", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at MRD", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#OS by ctDNA status at MRD
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA at MRD", ylab= "Overall Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24, 36))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at MRD", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Living Status",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#PFS by ctDNA status at MRD - exclude pts with adjuvant treatment post-MRD
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
excluded_ids <- c("UNM-007", "UNM-008", "UNM-023", "UNM-027", "UNM-029", 
                  "UNM-030", "UNM-035", "UNM-045", "UNM-051", "UNM-059", 
                  "UNM-075", "UNM-082", "UNM-032", "UNM-042", "UNM-043", 
                  "UNM-048", "UNM-050", "UNM-070")
circ_data <- circ_data[!circ_data$PatientID %in% excluded_ids, ]

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at MRD", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at MRD", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#OS by ctDNA status at MRD - exclude pts with adjuvant treatment post-MRD
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
excluded_ids <- c("UNM-007", "UNM-008", "UNM-023", "UNM-027", "UNM-029", 
                  "UNM-030", "UNM-035", "UNM-045", "UNM-051", "UNM-059", 
                  "UNM-075", "UNM-082", "UNM-032", "UNM-042", "UNM-043", 
                  "UNM-048", "UNM-050", "UNM-070")
circ_data <- circ_data[!circ_data$PatientID %in% excluded_ids, ]

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA at MRD", ylab= "Overall Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24, 36))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at MRD", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Living Status",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#PFS by ctDNA status at MRD Stage I/II
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$cStage=="I/II",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at MRD Stage I/II", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at MRD Stage I/II", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#PFS by ctDNA status at MRD Stage III/IV
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$cStage=="III/IV",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at MRD Stage III/IV", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at MRD Stage I/II", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#PFS by ctDNA at MRD p16(+)
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$p16.status=="Positive",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at MRD p16(+)", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at MRD p16(+)", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#PFS by ctDNA at MRD p16(-)
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$p16.status=="Negative",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at MRD p16(-)", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at MRD p16(-)", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#PFS by ctDNA status at surveillance
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.Surveillance, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at Surveillance", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at Surveillance", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#Median numbers of time points and lead time in the longitudinal setting
```{r}
# Load the dataset
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)

median_Nsurvtps <- median(circ_datadf$Nsurvtps, na.rm = TRUE)
min_Nsurvtps <- min(circ_datadf$Nsurvtps, na.rm = TRUE)
max_Nsurvtps <- max(circ_datadf$Nsurvtps, na.rm = TRUE)

cat(sprintf("Median # of surveillance time points: %d (%d-%d)\n", 
            median_Nsurvtps, min_Nsurvtps, max_Nsurvtps))

circ_datadf$LeadTime_Months <- circ_datadf$LeadTime / 30.437
median_LeadTime <- median(circ_datadf$LeadTime_Months, na.rm = TRUE)
min_LeadTime <- min(circ_datadf$LeadTime_Months, na.rm = TRUE)
max_LeadTime <- max(circ_datadf$LeadTime_Months, na.rm = TRUE)
cat(sprintf("Longitudinally, ctDNA positivity preceded progression by a median of %.2f mo (%.2f–%.2f)\n", 
            median_LeadTime, min_LeadTime, max_LeadTime))
```

#Time-dependent analysis for PFS in longitudinal time points
```{r}
rm(list=ls())
setwd("~/Downloads")
dt <- read_xlsx("CLIA HNSCC Peddada Clinical Data_Time dependent.xlsx") |>
  clean_names() |>
  mutate(across(.cols = c(window_start_date,dfs_date,
                          surveillance_1_date:surveillance_12_date), 
                .fns = ~ as_date(as.Date(.x, format = "%Y-%m-%d"))))

dt_biomarker <- dt |>
  select(pts_id, ct_dna_surveillance_available,
         window_start_date,
         surveillance_1_status:surveillance_12_date) |>
  filter(ct_dna_surveillance_available) |>
  pivot_longer(cols = surveillance_1_status:surveillance_12_date,
               names_to = c("visit_number", ".value"),
               names_pattern = "surveillance_(.)_(.*)") |>
  mutate(biomarker_time = day(days(date - window_start_date))) |>
  select(pts_id, biomarker_time, biomarker_status = status) |>
  filter(!is.na(biomarker_time))

glimpse(dt_biomarker)

dt_survival <- dt |>
  select(pts_id, ct_dna_surveillance_available,
         window_start_date:dfs_date, dfs_event) |>  # Added dfs_event here
  filter(ct_dna_surveillance_available) |>
  mutate(dfs_time = (dfs_date - window_start_date),
         dfs_time = day(days(dfs_time)),
         dfs_event = as.numeric(dfs_event)) |>
  select(pts_id, dfs_time, dfs_event)

glimpse(dt_survival)

aux <- dt_survival %>% 
  filter(dfs_time <= 0)

tab <- left_join(aux, dt) |>
  select(pts_id, window_start_date, dfs_time, dfs_date,
         surveillance_1_date:surveillance_12_date) |>
  mutate(across(.cols = dfs_date:surveillance_12_date, 
                .fns = ~ as_date(.x))) |>
  select(pts_id, window_start_date, dfs_date, dfs_time)

datatable(tab, filter = "top")

dt_survival <- dt_survival |>
  filter(dfs_time > 0)

aux <- dt |>
  select(pts_id, ct_dna_surveillance_available,
         window_start_date, dfs_date,
         surveillance_1_date:surveillance_12_date) |>
  mutate(across(.cols = surveillance_1_date:surveillance_12_date, 
                .fns = ~ .x - window_start_date)) |>
  mutate(across(.cols = surveillance_1_date:surveillance_12_date, 
                .fns = ~ .x < 0)) |>
  rowwise() |>
  mutate(sum_neg = 
           sum(c_across(surveillance_1_date:surveillance_12_date),
               na.rm = TRUE))  |>
  select(pts_id, sum_neg)

tab <- left_join(aux, dt) |>
  filter(sum_neg > 0) |>
  select(pts_id, sum_neg, window_start_date,
         surveillance_1_date:surveillance_12_date) |>
  mutate(across(.cols = window_start_date:surveillance_12_date, 
                .fns = ~ as_date(.x))) 

datatable(tab, filter = "top")

aux <- dt |>
  select(pts_id, ct_dna_surveillance_available,
         window_start_date, dfs_date,
         surveillance_1_date:surveillance_12_date) |>
  mutate(across(.cols = dfs_date:surveillance_12_date, 
                .fns = ~ .x - window_start_date)) |>
  mutate(across(.cols = surveillance_2_date:surveillance_12_date,
                .fns = ~ dfs_date < .x)) |>
  rowwise() |>
  mutate(n_biomarker_after_event = sum(c_across(surveillance_2_date:
                                                  surveillance_12_date), 
                                       na.rm = TRUE)) |>
  mutate(across(.cols = surveillance_1_date:surveillance_12_date,
                .fns = ~ !is.na(.x))) |>
  mutate(total_biomarker = sum(c_across(surveillance_2_date:
                                          surveillance_12_date), 
                               na.rm = TRUE)) |>
  select(pts_id, n_biomarker_after_event, total_biomarker)

temp <- aux |> 
  select(-pts_id) |> 
  group_by(n_biomarker_after_event, total_biomarker) |>  # Direct grouping
  summarise(freq = n(), .groups = "drop")  # Drop groups after summarization


tab <- left_join(aux, dt) |>
  select(pts_id, n_biomarker_after_event, total_biomarker, 
         dfs_date,
         surveillance_2_date:surveillance_12_date) |>
  mutate(across(.cols = dfs_date:surveillance_12_date, 
                .fns = ~ as_date(.x))) |>
  filter(n_biomarker_after_event > 0)
datatable(tab, filter = "top")

aux <- tmerge(data1 = dt_survival, 
              data2 = dt_survival,
              id = pts_id, 
              dfs_event = event(dfs_time, dfs_event))
dt_final <- tmerge(data1 = aux, 
                   data2 = dt_biomarker,
                   id = pts_id, 
                   biomarker_status = 
                     tdc(biomarker_time, biomarker_status))

datatable(dt_final, filter = "top")

# Syntax if there is not time-dependent covariate
# fit <- coxph(Surv(dfs_time, dfs_event) ~ biomarker_status,
#              data = dt_final)
# summary(fit)

fit <- coxph(Surv(tstart, tstop, dfs_event) ~ biomarker_status,
             data = dt_final)
summary(fit)
cox_fit_summary <- summary(fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```


#PFS by ctDNA status at surveillance Stage I/II
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$cStage=="I/II",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.Surveillance, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = TRUE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at Surveillance Stage I/II", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxphf(surv_object ~ ctDNA.Surveillance, data=circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at Surveillance Stage I/II", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#PFS by ctDNA status at surveillance Stage III/IV
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$cStage=="III/IV",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.Surveillance, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at Surveillance Stage III/IV", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at Surveillance Stage III/IV", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#PFS by ctDNA status at surveillance p16(+)
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$p16.status=="Positive",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.Surveillance, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = TRUE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at Surveillance p16(+)", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxphf(surv_object ~ ctDNA.Surveillance, data=circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at Surveillance p16(+)", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#PFS by ctDNA status at surveillance p16(-)
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$p16.status=="Negative",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.Surveillance, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at Surveillance p16(-)", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at Surveillance p16(-)", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#Multivariate cox regression for PFS ctDNA status at surveillance
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]

circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"), labels = c("Negative", "Positive"))
circ_data$cStage <- factor(circ_data$cStage, levels = c("I/II", "III/IV"))
circ_data$p16.status <- factor(circ_data$p16.status, levels = c("Negative", "Positive"))
circ_data$Prim.Location <- factor(circ_data$Prim.Location, levels = c("Oropharynx", "Larynx/Hypopharynx", "Oral cavity", "Other (paranasal sinus and nasopharyngeal)"))
surv_object <- Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event) 
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance + cStage + p16.status + Prim.Location, data=circ_data) 
ggforest(cox_fit, data = circ_data, main = "Multivariate Regression Model for PFS", refLabel = "Reference Group")
test.ph <- cox.zph(cox_fit)
```


#ctDNA and MTM/mL Dynamics for pts at surveillance window
```{r}
#Dynamics and MTM/mL plots for patients with ctDNA negative at surveillance
rm(list=ls())
setwd("~/Downloads")
df <- read.csv("CLIA HNSCC ctDNA MTM.csv", stringsAsFactors = FALSE)
df <- df[df$ctDNA.Surveillance=="NEGATIVE",]

df$PFS.Event <- ifelse(df$PFS.Event %in% c("No", "no", "FALSE", "False", "0"), FALSE,
                       ifelse(df$PFS.Event %in% c("Yes", "yes", "TRUE", "True", "1"), TRUE, NA))
df$PFS.Event <- factor(df$PFS.Event, levels = c(FALSE, TRUE))
df <- df %>%
  group_by(PatientName) %>%
  filter(n() >= 2) %>% #keep only pts with at least 2 post-surgery time points
  ungroup()

num_unique <- length(unique(df$PatientName))
cat("Number of unique patients:", num_unique, "\n")

df_patient_pfs <- df %>%
  group_by(PatientName) %>%
  dplyr::summarize(
    PFS_True = any(PFS.Event == TRUE, na.rm = TRUE),
    PFS_False = all(PFS.Event == FALSE, na.rm = TRUE)
  )

num_true <- sum(df_patient_pfs$PFS_True)
num_false <- sum(df_patient_pfs$PFS_False)

cat("Number of unique patients with Event:", num_true, "\n")
cat("Number of unique patients with No Event:", num_false, "\n")

p <- ggplot(df, aes(x = date.diff.months, 
                    y = MTM.mL, 
                    group = PatientName, 
                    color = PFS.Event)) +
  geom_line() +      # Connect timepoints for each patient
  geom_point() +     # Add points for each timepoint
  # Use a log10 scale for the y-axis with specified breaks
  scale_y_log10(breaks = c(0.01, 0.1, 1, 10, 100),
                labels = c("0.01","0.1", "1", "10", "100")) +
  scale_x_continuous(breaks = seq(0, max(df$date.diff.months, na.rm = TRUE), by = 6)) +
  scale_color_manual(values = c("FALSE" = "blue", "TRUE" = "red")) +
  labs(
    x = "Time Since Surgery or start of definitive treatment (months)",
    y = "Mean Tumor Molecules per mL (MTM/mL)",
    color = "PFS Event"
  ) +
  theme_minimal()
print(p)

#Dynamics and MTM/mL plots for patients with ctDNA positive at surveillance
rm(list=ls())
setwd("~/Downloads")
df <- read.csv("CLIA HNSCC ctDNA MTM.csv", stringsAsFactors = FALSE)
df <- df[df$ctDNA.Surveillance=="POSITIVE",]

df$PFS.Event <- ifelse(df$PFS.Event %in% c("No", "no", "FALSE", "False", "0"), FALSE,
                       ifelse(df$PFS.Event %in% c("Yes", "yes", "TRUE", "True", "1"), TRUE, NA))
df$PFS.Event <- factor(df$PFS.Event, levels = c(FALSE, TRUE))
df <- df %>%
  group_by(PatientName) %>%
  filter(n() >= 2) %>% #keep only pts with at least 2 post-surgery time points
  ungroup()

num_unique <- length(unique(df$PatientName))
cat("Number of unique patients:", num_unique, "\n")

df_patient_pfs <- df %>%
  group_by(PatientName) %>%
  dplyr::summarize(
    PFS_True = any(PFS.Event == TRUE, na.rm = TRUE),
    PFS_False = all(PFS.Event == FALSE, na.rm = TRUE)
  )

num_true <- sum(df_patient_pfs$PFS_True)
num_false <- sum(df_patient_pfs$PFS_False)

cat("Number of unique patients with Event:", num_true, "\n")
cat("Number of unique patients with No Event:", num_false, "\n")

p <- ggplot(df, aes(x = date.diff.months, 
                    y = MTM.mL, 
                    group = PatientName, 
                    color = PFS.Event)) +
  geom_line() +      # Connect timepoints for each patient
  geom_point() +     # Add points for each timepoint
  # Use a log10 scale for the y-axis with specified breaks
  scale_y_log10(breaks = c(0.01, 0.1, 1, 10, 100),
                labels = c("0.01","0.1", "1", "10", "100")) +
  scale_x_continuous(breaks = seq(0, max(df$date.diff.months, na.rm = TRUE), by = 6)) +
  scale_color_manual(values = c("FALSE" = "blue", "TRUE" = "red")) +
  labs(
    x = "Time Since Surgery or start of definitive treatment (months)",
    y = "Mean Tumor Molecules per mL (MTM/mL)",
    color = "PFS Event"
  ) +
  theme_minimal()
print(p)
```

#PFS by ctDNA status at surveillance for pts with MRD & Surveillance time points available
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.complete=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.Surveillance, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA at Surveillance", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status at Surveillance", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#Time-dependent analysis for PFS in longitudinal time points for pts with MRD & Surveillance time points available
```{r}
rm(list=ls())
setwd("~/Downloads")
dt <- read_xlsx("CLIA HNSCC Peddada Clinical Data_Time dependent.xlsx") |>
  clean_names() |>
  mutate(across(.cols = c(window_start_date,dfs_date,
                          surveillance_1_date:surveillance_12_date), 
                .fns = ~ as_date(as.Date(.x, format = "%Y-%m-%d"))))

dt_biomarker <- dt |>
  select(pts_id, ct_dna_complete,
         window_start_date,
         surveillance_1_status:surveillance_12_date) |>
  filter(ct_dna_complete) |>
  pivot_longer(cols = surveillance_1_status:surveillance_12_date,
               names_to = c("visit_number", ".value"),
               names_pattern = "surveillance_(.)_(.*)") |>
  mutate(biomarker_time = day(days(date - window_start_date))) |>
  select(pts_id, biomarker_time, biomarker_status = status) |>
  filter(!is.na(biomarker_time))

glimpse(dt_biomarker)

dt_survival <- dt |>
  select(pts_id, ct_dna_complete,
         window_start_date:dfs_date, dfs_event) |>  # Added dfs_event here
  filter(ct_dna_complete) |>
  mutate(dfs_time = (dfs_date - window_start_date),
         dfs_time = day(days(dfs_time)),
         dfs_event = as.numeric(dfs_event)) |>
  select(pts_id, dfs_time, dfs_event)

glimpse(dt_survival)

aux <- dt_survival %>% 
  filter(dfs_time <= 0)

tab <- left_join(aux, dt) |>
  select(pts_id, window_start_date, dfs_time, dfs_date,
         surveillance_1_date:surveillance_12_date) |>
  mutate(across(.cols = dfs_date:surveillance_12_date, 
                .fns = ~ as_date(.x))) |>
  select(pts_id, window_start_date, dfs_date, dfs_time)

datatable(tab, filter = "top")

dt_survival <- dt_survival |>
  filter(dfs_time > 0)

aux <- dt |>
  select(pts_id, ct_dna_complete,
         window_start_date, dfs_date,
         surveillance_1_date:surveillance_12_date) |>
  mutate(across(.cols = surveillance_1_date:surveillance_12_date, 
                .fns = ~ .x - window_start_date)) |>
  mutate(across(.cols = surveillance_1_date:surveillance_12_date, 
                .fns = ~ .x < 0)) |>
  rowwise() |>
  mutate(sum_neg = 
           sum(c_across(surveillance_1_date:surveillance_12_date),
               na.rm = TRUE))  |>
  select(pts_id, sum_neg)

tab <- left_join(aux, dt) |>
  filter(sum_neg > 0) |>
  select(pts_id, sum_neg, window_start_date,
         surveillance_1_date:surveillance_12_date) |>
  mutate(across(.cols = window_start_date:surveillance_12_date, 
                .fns = ~ as_date(.x))) 

datatable(tab, filter = "top")

aux <- dt |>
  select(pts_id, ct_dna_complete,
         window_start_date, dfs_date,
         surveillance_1_date:surveillance_12_date) |>
  mutate(across(.cols = dfs_date:surveillance_12_date, 
                .fns = ~ .x - window_start_date)) |>
  mutate(across(.cols = surveillance_2_date:surveillance_12_date,
                .fns = ~ dfs_date < .x)) |>
  rowwise() |>
  mutate(n_biomarker_after_event = sum(c_across(surveillance_2_date:
                                                  surveillance_12_date), 
                                       na.rm = TRUE)) |>
  mutate(across(.cols = surveillance_1_date:surveillance_12_date,
                .fns = ~ !is.na(.x))) |>
  mutate(total_biomarker = sum(c_across(surveillance_2_date:
                                          surveillance_12_date), 
                               na.rm = TRUE)) |>
  select(pts_id, n_biomarker_after_event, total_biomarker)

temp <- aux |> 
  select(-pts_id) |> 
  group_by(n_biomarker_after_event, total_biomarker) |>  # Direct grouping
  summarise(freq = n(), .groups = "drop")  # Drop groups after summarization


tab <- left_join(aux, dt) |>
  select(pts_id, n_biomarker_after_event, total_biomarker, 
         dfs_date,
         surveillance_2_date:surveillance_12_date) |>
  mutate(across(.cols = dfs_date:surveillance_12_date, 
                .fns = ~ as_date(.x))) |>
  filter(n_biomarker_after_event > 0)
datatable(tab, filter = "top")

aux <- tmerge(data1 = dt_survival, 
              data2 = dt_survival,
              id = pts_id, 
              dfs_event = event(dfs_time, dfs_event))
dt_final <- tmerge(data1 = aux, 
                   data2 = dt_biomarker,
                   id = pts_id, 
                   biomarker_status = 
                     tdc(biomarker_time, biomarker_status))

datatable(dt_final, filter = "top")

# Syntax if there is not time-dependent covariate
# fit <- coxph(Surv(dfs_time, dfs_event) ~ biomarker_status,
#              data = dt_final)
# summary(fit)

fit <- coxph(Surv(tstart, tstop, dfs_event) ~ biomarker_status,
             data = dt_final)
summary(fit)
cox_fit_summary <- summary(fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```


#Median numbers of time points and lead time in the longitudinal setting for pts with MRD & Surveillance time points available
```{r}
# Load the dataset
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.complete=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_datadf <- as.data.frame(circ_data)

median_Nsurvtps <- median(circ_datadf$Nsurvtps, na.rm = TRUE)
min_Nsurvtps <- min(circ_datadf$Nsurvtps, na.rm = TRUE)
max_Nsurvtps <- max(circ_datadf$Nsurvtps, na.rm = TRUE)

cat(sprintf("Median # of surveillance time points: %d (%d-%d)\n", 
            median_Nsurvtps, min_Nsurvtps, max_Nsurvtps))

circ_datadf$LeadTime_Months <- circ_datadf$LeadTime / 30.437
median_LeadTime <- median(circ_datadf$LeadTime_Months, na.rm = TRUE)
min_LeadTime <- min(circ_datadf$LeadTime_Months, na.rm = TRUE)
max_LeadTime <- max(circ_datadf$LeadTime_Months, na.rm = TRUE)
cat(sprintf("Longitudinally, ctDNA positivity preceded progression by a median of %.2f mo (%.2f–%.2f)\n", 
            median_LeadTime, min_LeadTime, max_LeadTime))
```

#PFS by ctDNA status anytime
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.anytime!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)~ctDNA.anytime, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.anytime) %>%
  summarise(
    Total = n(),
    Events = sum(PFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PFS.months, event = circ_data$PFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.anytime, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PFS - ctDNA anytime", ylab= "Progression-Free Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 12, 24, 36))
circ_data$ctDNA.anytime <- factor(circ_data$ctDNA.anytime, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.anytime, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$ctDNA.anytime <- factor(circ_data$ctDNA.anytime, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$PFS.Event <- factor(circ_data$PFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Progression", "Progression"))
contingency_table <- table(circ_data$ctDNA.anytime, circ_data$PFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status anytime", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Progression",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Progression" = "blue", "Progression" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#OS by ctDNA status anytime
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.available=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.anytime!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.anytime, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.anytime) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.anytime, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA anytime", ylab= "Overall Survival", xlab="Months from Definitive Treatment", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24, 36))
circ_data$ctDNA.anytime <- factor(circ_data$ctDNA.anytime, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.anytime, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$ctDNA.anytime <- factor(circ_data$ctDNA.anytime, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.anytime, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA status anytime", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Living Status",
       caption = paste("Fisher's exact test p-value: ", format.pval(fisher_exact_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Progression label size
```

#Median numbers of time points and lead time anytime post-surery or definitive treatment
```{r}
# Load the dataset
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("CLIA HNSCC Peddada Clinical Data.csv")
circ_data <- circ_data[circ_data$ctDNA.anytime!="",]
circ_datadf <- as.data.frame(circ_data)

median_Nsurvtps <- median(circ_datadf$Ntotaltps, na.rm = TRUE)
min_Nsurvtps <- min(circ_datadf$Ntotaltps, na.rm = TRUE)
max_Nsurvtps <- max(circ_datadf$Ntotaltps, na.rm = TRUE)

cat(sprintf("Median # of time points anytimes: %d (%d-%d)\n", 
            median_Nsurvtps, min_Nsurvtps, max_Nsurvtps))

circ_datadf$LeadTime_Months <- circ_datadf$Anytime.LeadTime / 30.437
median_LeadTime <- median(circ_datadf$LeadTime_Months, na.rm = TRUE)
min_LeadTime <- min(circ_datadf$LeadTime_Months, na.rm = TRUE)
max_LeadTime <- max(circ_datadf$LeadTime_Months, na.rm = TRUE)
cat(sprintf("Anytime post-surgery or start of definitive treatment, ctDNA positivity preceded progression by a median of %.2f mo (%.2f–%.2f)\n", 
            median_LeadTime, min_LeadTime, max_LeadTime))
```
